B_VAE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "source": [
    "# from google.colab import drive\n",
    "# drive.mount('/content/drive')"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SfaVuMnEPkqX",
    "outputId": "e3ee43fc-0afc-411d-bb67-08efa94883a1"
   },
   "id": "SfaVuMnEPkqX",
   "execution_count": 1,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Mounted at /content/drive\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "!pip install open3d"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iqBagabUPsg-",
    "outputId": "1e7b57be-bbd9-4d2d-b22f-d711ed3ac26b"
   },
   "id": "iqBagabUPsg-",
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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      "\u001B[?25hInstalling collected packages: dash-table, dash-html-components, dash-core-components, addict, widgetsnbextension, werkzeug, retrying, pyquaternion, jedi, configargparse, comm, Flask, ipywidgets, dash, open3d\n",
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     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "id": "initial_id",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:08.790501Z",
     "start_time": "2025-01-04T08:23:08.785218Z"
    }
   },
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "id": "371b8c5b261a5b2c"
   },
   "cell_type": "markdown",
   "source": [
    "Encoder"
   ],
   "id": "371b8c5b261a5b2c"
  },
  {
   "metadata": {
    "id": "c75383b54d3cec85",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:09.074245Z",
     "start_time": "2025-01-04T08:23:09.067370Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PointNetPPEncoder(nn.Module):\n",
    "    def __init__(self, latent_dim=128):\n",
    "        super(PointNetPPEncoder, self).__init__()\n",
    "        self.latent_dim = latent_dim\n",
    "\n",
    "        # Shared MLPs for local feature extraction\n",
    "        self.conv1 = nn.Conv1d(3, 64, 1)\n",
    "        self.conv2 = nn.Conv1d(64, 128, 1)\n",
    "        self.conv3 = nn.Conv1d(128, 256, 1)\n",
    "        self.conv4 = nn.Conv1d(256, 512, 1)\n",
    "        self.conv5 = nn.Conv1d(512, 1024, 1)\n",
    "        self.conv6 = nn.Conv1d(1024, 2048, 1)\n",
    "\n",
    "        # Fully connected layers for global features\n",
    "        self.fc1 = nn.Linear(2048, 1024)\n",
    "        self.fc2 = nn.Linear(1024, 512)\n",
    "        self.fc3 = nn.Linear(512, 256)\n",
    "        self.fc_mu = nn.Linear(256, latent_dim)       # Mean of latent distribution\n",
    "        self.fc_log_sigma = nn.Linear(256, latent_dim)  # Log variance of latent distribution\n",
    "\n",
    "    def forward(self, x):\n",
    "        # Input x: (batch_size, num_points, 3)\n",
    "        x = x.permute(0, 2, 1)  # Change to (batch_size, 3, num_points)\n",
    "\n",
    "        # Apply shared MLPs\n",
    "        x = F.relu(self.conv1(x))\n",
    "        x = F.relu(self.conv2(x))\n",
    "        x = F.relu(self.conv3(x))\n",
    "        x = F.relu(self.conv4(x))\n",
    "        x = F.relu(self.conv5(x))\n",
    "        x = F.relu(self.conv6(x))\n",
    "\n",
    "        # Global max pooling\n",
    "        x = torch.max(x, dim=2)[0]  # (batch_size, 256)\n",
    "\n",
    "        # Fully connected layers for latent representation\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.relu(self.fc3(x))\n",
    "        mu = self.fc_mu(x)           # (batch_size, latent_dim)\n",
    "        log_sigma = self.fc_log_sigma(x)  # (batch_size, latent_dim)\n",
    "        return mu, log_sigma"
   ],
   "id": "c75383b54d3cec85",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "id": "61f36e88b8196ee9"
   },
   "cell_type": "markdown",
   "source": [
    "Decoder"
   ],
   "id": "61f36e88b8196ee9"
  },
  {
   "metadata": {
    "id": "64d7136b58bb5243",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:09.331927Z",
     "start_time": "2025-01-04T08:23:09.325553Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PointNetPPDecoder(nn.Module):\n",
    "    def __init__(self, latent_dim=128, num_points=4096):\n",
    "        super(PointNetPPDecoder, self).__init__()\n",
    "        self.latent_dim = latent_dim\n",
    "        self.num_points = num_points\n",
    "\n",
    "        # Fully connected layers to create an initial coarse point cloud\n",
    "        self.fc1 = nn.Linear(latent_dim, 256)\n",
    "        self.fc2 = nn.Linear(256, 512)\n",
    "        self.fc3 = nn.Linear(512, 1024)  # Coarse point cloud (e.g., 256 points * 3)\n",
    "\n",
    "        # Upsampling layers for finer details\n",
    "        self.fc4 = nn.Linear(1024, 2048)\n",
    "        self.fc5 = nn.Linear(2048, 4096)\n",
    "        self.fc6 = nn.Linear(4096, num_points * 3) # Final high-resolution output\n",
    "\n",
    "    def forward(self, z):\n",
    "        # Input z: (batch_size, latent_dim)\n",
    "        x = F.relu(self.fc1(z))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = F.relu(self.fc3(x))  # Coarse point cloud\n",
    "\n",
    "        # Upsample to finer resolution\n",
    "        x = F.relu(self.fc4(x))\n",
    "        x = F.relu(self.fc5(x))\n",
    "        x = self.fc6(x)  # (batch_size, num_points * 3)\n",
    "\n",
    "        # Reshape to point cloud format\n",
    "        x = x.view(-1, self.num_points, 3)  # (batch_size, num_points, 3)\n",
    "        return x"
   ],
   "id": "64d7136b58bb5243",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "id": "758790acbb9d4d71"
   },
   "cell_type": "markdown",
   "source": [
    "Reparameterization trick"
   ],
   "id": "758790acbb9d4d71"
  },
  {
   "metadata": {
    "id": "13b003faaa5cf90a",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:09.744579Z",
     "start_time": "2025-01-04T08:23:09.740285Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def reparameterize(mu, log_sigma):\n",
    "    \"\"\"\n",
    "    Reparameterization trick to sample latent variable z.\n",
    "    z = mu + eps * exp(log_sigma * 0.5)\n",
    "    \"\"\"\n",
    "    std = torch.exp(0.5 * log_sigma)\n",
    "    epsilon = torch.randn_like(std)\n",
    "    return mu + epsilon * std"
   ],
   "id": "13b003faaa5cf90a",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {
    "id": "f9bd0df85b8a947e"
   },
   "cell_type": "markdown",
   "source": [
    "Encoder + Decoder + Reparameterization Trick = Autoencoder (in our case, Variational Autoencoder because we have mu and log sigma)"
   ],
   "id": "f9bd0df85b8a947e"
  },
  {
   "metadata": {
    "id": "5bfd8c367e909d47",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:10.064001Z",
     "start_time": "2025-01-04T08:23:10.059181Z"
    }
   },
   "cell_type": "code",
   "source": [
    "class PointNetPPAutoencoder(nn.Module):\n",
    "    def __init__(self, latent_dim=128, num_points=4096):\n",
    "        super(PointNetPPAutoencoder, self).__init__()\n",
    "        self.encoder = PointNetPPEncoder(latent_dim)\n",
    "        self.decoder = PointNetPPDecoder(latent_dim, num_points)\n",
    "\n",
    "    def forward(self, x):\n",
    "        \"\"\"\n",
    "        Returns:\n",
    "          reconstructed: (batch_size, num_points, 3)\n",
    "          mu: (batch_size, latent_dim)\n",
    "          log_sigma: (batch_size, latent_dim)\n",
    "        \"\"\"\n",
    "        mu, log_sigma = self.encoder(x)\n",
    "        z = reparameterize(mu, log_sigma)  # <-- Use the reparam trick\n",
    "        reconstructed = self.decoder(z)\n",
    "        return reconstructed, mu, log_sigma"
   ],
   "id": "5bfd8c367e909d47",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "id": "c418aab24fb6cd1a"
   },
   "cell_type": "markdown",
   "source": [
    "Loss Functions"
   ],
   "id": "c418aab24fb6cd1a"
  },
  {
   "metadata": {
    "id": "5937ef03469c762",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:10.342795Z",
     "start_time": "2025-01-04T08:23:10.338348Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def chamfer_distance(x, y):\n",
    "    \"\"\"\n",
    "    Compute Chamfer Distance between two point clouds x and y.\n",
    "    \"\"\"\n",
    "    x_expanded = x.unsqueeze(2)  # (batch_size, num_points_x, 1, 3)\n",
    "    y_expanded = y.unsqueeze(1)  # (batch_size, 1, num_points_y, 3)\n",
    "    distances = torch.norm(x_expanded - y_expanded, dim=3)  # Pairwise distances\n",
    "    min_dist_x = torch.min(distances, dim=2)[0]\n",
    "    min_dist_y = torch.min(distances, dim=1)[0]\n",
    "    return torch.mean(min_dist_x) + torch.mean(min_dist_y)\n",
    "\n",
    "def kl_divergence(mu, log_sigma):\n",
    "    \"\"\"\n",
    "    Compute the KL divergence between the encoder's latent distribution and a standard Gaussian.\n",
    "    Args:\n",
    "        mu: Mean of the latent distribution (batch_size, latent_dim)\n",
    "        log_sigma: Log of standard deviation (batch_size, latent_dim)\n",
    "    Returns:\n",
    "        kl_loss: The KL divergence loss (scalar)\n",
    "    \"\"\"\n",
    "    kl_loss = -0.5 * torch.sum(1 + log_sigma - mu.pow(2) - log_sigma.exp(), dim=1)\n",
    "    return torch.mean(kl_loss)  # Return the batch mean"
   ],
   "id": "5937ef03469c762",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "id": "e5489591ca3a5bfe",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:10.703752Z",
     "start_time": "2025-01-04T08:23:10.693839Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.utils.data as data\n",
    "import open3d as o3d\n",
    "import os\n",
    "import numpy as np\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "class PointCloudDataset(data.Dataset):\n",
    "    def __init__(self, folder_path, num_points=4096):\n",
    "        self.folder_path = folder_path\n",
    "        self.num_points = num_points\n",
    "        self.files = [f for f in os.listdir(folder_path) if f.endswith('.ply')]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.files)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        file_path = os.path.join(self.folder_path, self.files[idx])\n",
    "        pcd = o3d.io.read_point_cloud(file_path)\n",
    "        points = np.asarray(pcd.points)\n",
    "\n",
    "        # Scale and center the point cloud to fit within a unit cube or sphere:\n",
    "        points -= np.mean(points, axis=0)  # Center\n",
    "        # points /= np.max(np.linalg.norm(points, axis=1))  # I chose not to scale since I would like to see if my latent space can predict for height\n",
    "\n",
    "        # Randomly sample or pad points to ensure uniformity\n",
    "        if points.shape[0] > self.num_points:\n",
    "            indices = np.random.choice(points.shape[0], self.num_points, replace=False)\n",
    "            points = points[indices]\n",
    "        elif points.shape[0] < self.num_points:\n",
    "            pad_size = self.num_points - points.shape[0]\n",
    "            pad_points = np.zeros((pad_size, 3))\n",
    "            points = np.vstack((points, pad_points))\n",
    "\n",
    "        return torch.tensor(points, dtype=torch.float32)\n"
   ],
   "id": "e5489591ca3a5bfe",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {
    "id": "f7eefcfeb45ad53e"
   },
   "cell_type": "markdown",
   "source": [
    "Data Loader"
   ],
   "id": "f7eefcfeb45ad53e"
  },
  {
   "metadata": {
    "id": "a1885fb241f2426",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:11.425216Z",
     "start_time": "2025-01-04T08:23:11.419422Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "from torch.utils.data import random_split\n",
    "\n",
    "def get_data_loaders(folder_path=\"/content/drive/MyDrive/buildings_pointcloud_ply\",\n",
    "                     num_points=4096,\n",
    "                     batch_size=32,\n",
    "                     train_ratio=0.9,\n",
    "                     random_seed=42):  # Added a seed parameter for reproducibility\n",
    "    # Set the random seed for reproducibility\n",
    "    torch.manual_seed(random_seed)\n",
    "\n",
    "    dataset = PointCloudDataset(folder_path, num_points=num_points)\n",
    "    train_size = int(train_ratio * len(dataset))\n",
    "    test_size = len(dataset) - train_size\n",
    "    train_dataset, test_dataset = random_split(dataset, [train_size, test_size])\n",
    "\n",
    "    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "    print(f\"Dataset split: {len(train_dataset)} train samples, {len(test_dataset)} test samples\")\n",
    "    return train_loader, test_loader\n",
    "\n"
   ],
   "id": "a1885fb241f2426",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {
    "id": "66eeedc6483e5548"
   },
   "cell_type": "markdown",
   "source": [
    "Define Training Loop"
   ],
   "id": "66eeedc6483e5548"
  },
  {
   "metadata": {
    "id": "97f721ebb7ec8945",
    "ExecuteTime": {
     "end_time": "2025-01-04T08:23:14.326735Z",
     "start_time": "2025-01-04T08:23:12.357286Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import torch.optim as optim\n",
    "\n",
    "def train_model(folder_path=\"/content/drive/MyDrive/buildings_pointcloud_ply\",\n",
    "                latent_dim=128,\n",
    "                num_points=4096,\n",
    "                batch_size=16,\n",
    "                num_epochs=50,\n",
    "                learning_rate=0.001,\n",
    "                beta=1.0,\n",
    "                device=\"cpu\"):\n",
    "\n",
    "    # Prepare data\n",
    "    train_loader, test_loader = get_data_loaders(\n",
    "        folder_path=folder_path,\n",
    "        num_points=num_points,\n",
    "        batch_size=batch_size,\n",
    "        train_ratio=0.9\n",
    "    )\n",
    "\n",
    "    # Initialize model\n",
    "    model = PointNetPPAutoencoder(latent_dim=latent_dim, num_points=num_points)\n",
    "    model.to(device)\n",
    "\n",
    "    # Optimizer\n",
    "    optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "    loss_history = []\n",
    "\n",
    "    print(\"Starting training...\")\n",
    "    for epoch in range(num_epochs):\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        progress_bar = tqdm(train_loader, desc=f\"Epoch {epoch + 1}/{num_epochs}\")\n",
    "\n",
    "        for batch in progress_bar:\n",
    "            batch = batch.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "\n",
    "            # Forward pass (with VAE components)\n",
    "            reconstructed, mu, log_sigma = model(batch)\n",
    "\n",
    "            # Compute losses\n",
    "            recon_loss = chamfer_distance(reconstructed, batch)\n",
    "            kld_loss = kl_divergence(mu, log_sigma)\n",
    "\n",
    "            # Total loss (VAE) (which is equal to B=1)\n",
    "            # loss = recon_loss + kld_loss\n",
    "\n",
    "            # Total loss (beta-VAE)\n",
    "            loss = recon_loss + beta * kld_loss\n",
    "\n",
    "            # Backward pass\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            epoch_loss += loss.item()\n",
    "            progress_bar.set_postfix({\"Batch Loss\": loss.item()})\n",
    "\n",
    "        avg_epoch_loss = epoch_loss / len(train_loader)\n",
    "        loss_history.append(avg_epoch_loss)\n",
    "        print(f\"Epoch {epoch + 1}/{num_epochs}, Loss: {avg_epoch_loss:.4f}\")\n",
    "\n",
    "        # Save the trained model\n",
    "        model_save_path = \"pointnetpp_B3-VAE.pth\"\n",
    "        torch.save(model.state_dict(), model_save_path)\n",
    "        print(f\"Model saved to {model_save_path}\")\n",
    "\n",
    "\n",
    "\n",
    "    # Plot loss history\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(range(1, num_epochs + 1), loss_history, marker='o', linestyle='-')\n",
    "    plt.title(\"Training Loss Over Epochs (Chamfer + KL)\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.grid()\n",
    "    plt.show()"
   ],
   "id": "97f721ebb7ec8945",
   "outputs": [],
   "execution_count": 11
  },
  {
   "metadata": {
    "id": "85b90f670ce754"
   },
   "cell_type": "markdown",
   "source": [
    "Train"
   ],
   "id": "85b90f670ce754"
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "5085853a66ecc7f6",
    "outputId": "c4ed092d-2f6e-4cec-9728-40ac7e8cf800"
   },
   "cell_type": "code",
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Dataset split: 3606 train samples, 401 test samples\n",
      "Starting training...\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 1/50: 100%|██████████| 226/226 [02:22<00:00,  1.59it/s, Batch Loss=6.21]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 1/50, Loss: 5.6576\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 2/50: 100%|██████████| 226/226 [00:26<00:00,  8.68it/s, Batch Loss=6.98]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 2/50, Loss: 5.2378\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 3/50: 100%|██████████| 226/226 [00:26<00:00,  8.39it/s, Batch Loss=9.28]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 3/50, Loss: 5.2100\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 4/50: 100%|██████████| 226/226 [00:26<00:00,  8.42it/s, Batch Loss=12.5]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 4/50, Loss: 5.1692\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 5/50: 100%|██████████| 226/226 [00:26<00:00,  8.42it/s, Batch Loss=3.81]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 5/50, Loss: 5.1355\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 6/50: 100%|██████████| 226/226 [00:26<00:00,  8.42it/s, Batch Loss=5.63]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 6/50, Loss: 5.1267\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 7/50: 100%|██████████| 226/226 [00:26<00:00,  8.41it/s, Batch Loss=6.39]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 7/50, Loss: 5.1264\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 8/50: 100%|██████████| 226/226 [00:26<00:00,  8.47it/s, Batch Loss=4.63]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 8/50, Loss: 5.1023\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 9/50: 100%|██████████| 226/226 [00:26<00:00,  8.51it/s, Batch Loss=3.42]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 9/50, Loss: 5.1028\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 10/50: 100%|██████████| 226/226 [00:26<00:00,  8.47it/s, Batch Loss=3.55]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 10/50, Loss: 5.0787\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 11/50: 100%|██████████| 226/226 [00:26<00:00,  8.41it/s, Batch Loss=3.39]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 11/50, Loss: 5.0872\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 12/50: 100%|██████████| 226/226 [00:26<00:00,  8.47it/s, Batch Loss=3.94]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 12/50, Loss: 5.0766\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "Epoch 13/50: 100%|██████████| 226/226 [00:26<00:00,  8.40it/s, Batch Loss=5.47]\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Epoch 13/50, Loss: 5.0756\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
     ]
    },
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      "Epoch 14/50, Loss: 5.0678\n",
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    },
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      "Epoch 15/50, Loss: 5.0752\n",
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     ]
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      "Epoch 16/50, Loss: 5.0695\n",
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      "Epoch 17/50, Loss: 5.0559\n",
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      "Epoch 18/50, Loss: 5.0558\n",
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      "Epoch 19/50, Loss: 5.0619\n",
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      "Epoch 20/50, Loss: 5.0536\n",
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      "Epoch 21/50, Loss: 5.0555\n",
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      "Epoch 22/50, Loss: 5.0551\n",
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      "Epoch 24/50, Loss: 5.0502\n",
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      "Epoch 25/50, Loss: 5.0461\n",
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      "Epoch 28/50, Loss: 5.0463\n",
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      "Epoch 29/50, Loss: 5.0530\n",
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      "Epoch 30/50, Loss: 5.0462\n",
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      "Epoch 37/50, Loss: 5.0487\n",
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      "Epoch 38/50, Loss: 5.0354\n",
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      "Epoch 39/50, Loss: 5.0492\n",
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      "Epoch 40/50, Loss: 5.0579\n",
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      "Epoch 42/50, Loss: 5.0752\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
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      "Epoch 43/50, Loss: 5.0430\n",
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      "Epoch 44/50, Loss: 5.0402\n",
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      "Epoch 45/50, Loss: 5.0361\n",
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      "Epoch 46/50, Loss: 5.0401\n",
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      "Epoch 47/50, Loss: 5.0377\n",
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      "Epoch 48/50, Loss: 5.0360\n",
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      "Epoch 49/50, Loss: 5.0425\n",
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      "Epoch 50/50, Loss: 5.0373\n",
      "Model saved to pointnetpp_B3-VAE.pth\n"
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    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "execution_count": 12,
   "source": [
    "train_model(\n",
    "    folder_path=\"/content/drive/MyDrive/buildings_pointcloud_ply\",\n",
    "    latent_dim=128,\n",
    "    num_points=4096,\n",
    "    batch_size=16,\n",
    "    num_epochs=50,\n",
    "    learning_rate=0.001,\n",
    "    beta=3.0,\n",
    "    device=\"cuda\"  # or \"cuda\" if you have a GPU\n",
    ")"
   ],
   "id": "5085853a66ecc7f6"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "Load the model and play around",
   "id": "985c3ba03253b407"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-06T23:45:55.376991Z",
     "start_time": "2025-01-06T23:45:49.691326Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import open3d as o3d\n",
    "import torch\n",
    "\n",
    "def visualize_input_and_reconstructed(model, test_loader, device=\"cpu\"):\n",
    "    model.eval()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        # Get the first batch from the test_loader\n",
    "        for batch in test_loader:\n",
    "            # Shape of batch: (batch_size, num_points, 3)\n",
    "            batch = batch.to(device)\n",
    "            break  # Just get the first batch\n",
    "\n",
    "        # Let's visualize the first point cloud in this batch\n",
    "        input_pc = batch[0]  # shape: (num_points, 3)\n",
    "        input_np = input_pc.cpu().numpy()  # Convert to numpy for open3d\n",
    "\n",
    "        # Encode and decode the point cloud\n",
    "        mu, log_sigma = model.encoder(input_pc.unsqueeze(0))  # add batch dim => (1, num_points, 3)\n",
    "        z = reparameterize(mu, log_sigma)\n",
    "        reconstructed = model.decoder(z)  # shape: (1, num_points, 3)\n",
    "\n",
    "        # Convert reconstructed to numpy\n",
    "        reconstructed_np = reconstructed[0].cpu().numpy()\n",
    "\n",
    "    # Create Open3D point cloud for the input\n",
    "    pcd_input = o3d.geometry.PointCloud()\n",
    "    pcd_input.points = o3d.utility.Vector3dVector(input_np)\n",
    "    # Color the input cloud red\n",
    "    pcd_input.paint_uniform_color([1.0, 0.0, 0.0])\n",
    "\n",
    "    # Create Open3D point cloud for the reconstructed shape\n",
    "    pcd_reconstructed = o3d.geometry.PointCloud()\n",
    "    pcd_reconstructed.points = o3d.utility.Vector3dVector(reconstructed_np)\n",
    "    # Color the reconstructed cloud green\n",
    "    pcd_reconstructed.paint_uniform_color([0.0, 1.0, 0.0])\n",
    "\n",
    "    # (Optional) Shift the reconstructed cloud so it’s easier to see side by side\n",
    "    shift_along_x = 1.0\n",
    "    pcd_reconstructed.translate((shift_along_x, 0, 0))\n",
    "\n",
    "    # Visualize both point clouds together\n",
    "    o3d.visualization.draw_geometries([pcd_input, pcd_reconstructed])"
   ],
   "id": "a3726b87f4b8163e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jupyter environment detected. Enabling Open3D WebVisualizer.\n",
      "[Open3D INFO] WebRTC GUI backend enabled.\n",
      "[Open3D INFO] WebRTCWindowSystem: HTTP handshake server disabled.\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-04T08:33:20.316443Z",
     "start_time": "2025-01-04T08:32:30.737533Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import open3d as o3d\n",
    "import torch\n",
    "\n",
    "model = PointNetPPAutoencoder(latent_dim=128, num_points=4096)\n",
    "model.load_state_dict(torch.load(\"pointnetpp_B3-VAE.pth\", map_location=torch.device(\"cpu\")))\n",
    "\n",
    "model.to(\"cpu\")  # or \"cuda\"\n",
    "\n",
    "train_loader, test_loader = get_data_loaders(\n",
    "    folder_path=r\"C:\\Users\\Admin\\PycharmProjects\\AI-Projects\\Tokyo-PointCloud\\buildings_pointcloud_ply\",\n",
    "    num_points=4096,\n",
    "    batch_size=4,  # or 1, if you want just one per batch\n",
    "    train_ratio=0.9\n",
    ")\n",
    "\n",
    "visualize_input_and_reconstructed(model, test_loader, device=\"cpu\")"
   ],
   "id": "3f2803c3a41a384b",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_31396\\2847674894.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load(\"pointnetpp_B3-VAE.pth\", map_location=torch.device(\"cpu\")))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset split: 3606 train samples, 401 test samples\n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The requested transformation operation is not supported. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The requested transformation operation is not supported. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The handle is invalid. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The requested transformation operation is not supported. \n",
      "[Open3D WARNING] GLFW Error: WGL: Failed to make context current: The handle is invalid. \n",
      "[Open3D WARNING] [ViewControl] SetViewPoint() failed because window height and width are not set.\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-04T08:51:20.742485Z",
     "start_time": "2025-01-04T08:40:55.216170Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# ---------------------------\n",
    "# 1. Load your trained (beta-)VAE model\n",
    "# ---------------------------\n",
    "\n",
    "# Load model\n",
    "model = PointNetPPAutoencoder(latent_dim=128, num_points=4096)\n",
    "model.load_state_dict(torch.load('pointnetpp_B3-VAE.pth', map_location=torch.device('cpu')))\n",
    "model.eval()\n",
    "\n",
    "# ---------------------------\n",
    "# 2. Prepare (or load) the dataset\n",
    "# ---------------------------\n",
    "# Assuming train_loader yields only `inputs`, which are point clouds.\n",
    "all_latents = []\n",
    "\n",
    "for inputs in train_loader:  # No labels\n",
    "    inputs = inputs.to('cpu')  # Use 'cuda' if you have a GPU and the model is on GPU\n",
    "    with torch.no_grad():\n",
    "        z_mean, z_logvar = model.encoder(inputs)  # Call the encoder directly\n",
    "\n",
    "        # Sample from Gaussian distribution using reparameterization trick\n",
    "        std = torch.exp(0.5 * z_logvar)\n",
    "        eps = torch.randn_like(std)  # Gaussian noise\n",
    "        z_vec = z_mean + eps * std  # Sampled latent vector\n",
    "\n",
    "    all_latents.append(z_vec.cpu().numpy())\n",
    "\n",
    "all_latents = np.concatenate(all_latents, axis=0)  # shape: (N, latent_dim)\n",
    "\n",
    "# ---------------------------\n",
    "# 3. Apply t-SNE (or UMAP) to reduce dimensionality\n",
    "# ---------------------------\n",
    "tsne = TSNE(n_components=2, random_state=42)\n",
    "latents_2d = tsne.fit_transform(all_latents)  # shape: (N, 2)\n",
    "\n",
    "# ---------------------------\n",
    "# 4. Plot the 2D t-SNE results\n",
    "# ---------------------------\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.scatter(latents_2d[:, 0], latents_2d[:, 1], s=5)  # No labels, so just plot the points\n",
    "plt.title(\"Latent Space Visualization (t-SNE)\")\n",
    "plt.xlabel(\"t-SNE Dim 1\")\n",
    "plt.ylabel(\"t-SNE Dim 2\")\n",
    "plt.show()"
   ],
   "id": "d92294d336726c0b",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_31396\\1838968711.py:12: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  model.load_state_dict(torch.load('pointnetpp_B3-VAE.pth', map_location=torch.device('cpu')))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
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