PointNet Model.ipynb

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   "source": [
    "import os\n",
    "import torch\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from torch import nn\n",
    "import torch.optim as optim\n",
    "import matplotlib.pyplot as plt\n",
    "from skimage import io, transform\n",
    "from torchvision import transforms, utils\n",
    "from torch.utils.data import Dataset, DataLoader"
   ]
  },
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    "tags": []
   },
   "source": [
    "## Custom Transform, Normalize & ToTensor"
   ]
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   "source": [
    "class Select_Points(object):\n",
    "    def __init__(self, output_size):\n",
    "        assert isinstance(output_size, int)\n",
    "        self.output_size = output_size\n",
    "\n",
    "    def __call__(self, mesh):\n",
    "        verts, faces = mesh\n",
    "        verts = np.array(verts)\n",
    "        areas = np.zeros((len(faces)))\n",
    "\n",
    "        for i in range(len(areas)):\n",
    "            a = np.linalg.norm(verts[faces[i][0]]-verts[faces[i][1]])\n",
    "            b = np.linalg.norm(verts[faces[i][1]]-verts[faces[i][2]])\n",
    "            c = np.linalg.norm(verts[faces[i][2]]-verts[faces[i][0]])\n",
    "            s = 0.5*(a+b+c)\n",
    "            areas[i] = max(s*(s-a)*(s-b)*(s-c),0)**0.5\n",
    "            \n",
    "        sampled_faces = (random.choices(faces, weights=areas, cum_weights=None, k=self.output_size))\n",
    "        sampled_points = np.zeros((self.output_size, 3))\n",
    "\n",
    "        for i in range(len(sampled_faces)):\n",
    "            point1, point2, point3 = (verts[sampled_faces[i][0]],verts[sampled_faces[i][1]], verts[sampled_faces[i][2]])\n",
    "            s, t = sorted([random.random(), random.random()])\n",
    "            func = lambda i: s * point1[i] + (t-s)*point2[i] + (1-t)*point3[i]\n",
    "            sampled_points[i] = (func(0), func(1), func(2))\n",
    "        return sampled_points\n",
    "    \n",
    "class Normalize(object):\n",
    "    def __call__(self, pointcloud):\n",
    "        assert len(pointcloud.shape)==2\n",
    "        norm_pointcloud = pointcloud - np.mean(pointcloud, axis=0) \n",
    "        norm_pointcloud /= np.max(np.linalg.norm(norm_pointcloud, axis=1))\n",
    "        return  norm_pointcloud\n",
    "    \n",
    "class ToTensor(object):\n",
    "    def __call__(self, pointcloud):\n",
    "        assert len(pointcloud.shape)==2\n",
    "        a = torch.from_numpy(pointcloud)\n",
    "        return a.type(torch.DoubleTensor)"
   ]
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    "tags": []
   },
   "source": [
    "## Custom DataSet"
   ]
  },
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   "source": [
    "class ModelNet_Dataset(Dataset):\n",
    "    def __init__(self, root_dir,  folder=None, transform=None):\n",
    "        self.transform = transform\n",
    "        folders = [dir for dir in sorted(os.listdir(root_dir)) if os.path.isdir(root_dir+\"/\"+dir)]\n",
    "        self.classes = {folder: i for i, folder in enumerate(folders)}\n",
    "        self.files = []\n",
    "        for cl in self.classes.keys():\n",
    "            new_dir = root_dir+\"/\"+cl+\"/\"+folder\n",
    "            for file in os.listdir(new_dir):\n",
    "                if file.endswith('.off'):\n",
    "                    sample = {}\n",
    "                    sample['Obj_Path'] = new_dir+\"/\"+file\n",
    "                    sample['Class'] = cl\n",
    "                    self.files.append(sample)\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.files)\n",
    "\n",
    "    def __getitem__(self, idx):            \n",
    "        Obj_Path = self.files[idx]['Obj_Path']\n",
    "        Class = self.files[idx]['Class']\n",
    "        Datapoint = None\n",
    "        with open(Obj_Path, 'r') as file:\n",
    "            off_header = file.readline().strip()\n",
    "            if 'OFF' == off_header:\n",
    "                data, n_faces, __ = tuple([int(s) for s in file.readline().strip().split(' ')])\n",
    "            else:\n",
    "                data, n_faces, __  = tuple([int(s) for s in off_header[3:].split(' ')])\n",
    "            data = [[float(s) for s in file.readline().strip().split(' ')] for i_vert in range(data)]\n",
    "            faces = [[int(s) for s in file.readline().strip().split(' ')][1:] for i_face in range(n_faces)]\n",
    "            if self.transform:\n",
    "                Datapoint = self.transform((data, faces))\n",
    "        return {\n",
    "            'Data': Datapoint, \n",
    "            'Class': self.classes[Class]\n",
    "        }"
   ]
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   },
   "source": [
    "## Model Architecture T.Net "
   ]
  },
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   "source": [
    "class T_Net(nn.Module):\n",
    "    def __init__(self, k=3):\n",
    "        super(T_Net, self).__init__()\n",
    "        self.k = k\n",
    "        self.ConvBlock = nn.Sequential(\n",
    "            nn.Conv1d(in_channels=k, out_channels=64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.MaxPool1d(kernel_size=2, stride=2),\n",
    "            \n",
    "            nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.Conv1d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.MaxPool1d(kernel_size=2, stride=2),\n",
    "            \n",
    "            nn.Conv1d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.Conv1d(in_channels=128, out_channels=128, kernel_size=3, padding=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.MaxPool1d(kernel_size=2, stride=2)\n",
    "        )\n",
    "        self.FC_Block = nn.Sequential(\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(16384, 4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(4096, 4096),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(4096, k*k),\n",
    "        )\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.ConvBlock(x)\n",
    "        out = self.FC_Block(out)\n",
    "        out = out.reshape(-1, self.k, self.k)\n",
    "        return out"
   ]
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    "tags": []
   },
   "source": [
    "## Model Architecture PointNet"
   ]
  },
  {
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   "source": [
    "class Point_Net(nn.Module):\n",
    "    def __init__(self, number_of_classes):\n",
    "        super(Point_Net, self).__init__()\n",
    "        self.T_Net3 = T_Net(3)\n",
    "        self.ConvSet_1 = nn.Sequential(\n",
    "            nn.Conv1d(in_channels=3, out_channels=32, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(32),\n",
    "            nn.Conv1d(in_channels=32, out_channels=64, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.Conv1d(in_channels=64, out_channels=64, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.MaxPool1d(kernel_size=1, stride=1)\n",
    "        )\n",
    "        self.T_Net64 = T_Net(64)\n",
    "        self.ConvSet_2 = nn.Sequential(\n",
    "            nn.Conv1d(in_channels=64, out_channels=64, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(64),\n",
    "            nn.Conv1d(in_channels=64, out_channels=128, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.Conv1d(in_channels=128, out_channels=128, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(128),\n",
    "            nn.Conv1d(in_channels=128, out_channels=1024, kernel_size=1),\n",
    "            nn.ReLU(),\n",
    "            nn.BatchNorm1d(1024),\n",
    "            nn.MaxPool1d(kernel_size=1, stride=1)\n",
    "        )\n",
    "        self.FC_Layers = nn.Sequential(\n",
    "            nn.MaxPool1d(1024),\n",
    "            nn.Flatten(),\n",
    "            nn.Linear(1024, 512),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(512, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(p=0.5),\n",
    "            nn.Linear(256, number_of_classes),\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        Tnet3 = self.T_Net3(x)\n",
    "        x = torch.bmm(torch.transpose(x,1,2), Tnet3).transpose(1,2)\n",
    "        x = self.ConvSet_1(x)\n",
    "        Tnet64 = self.T_Net64(x)\n",
    "        x = torch.bmm(torch.transpose(x,1,2), Tnet64).transpose(1,2)\n",
    "        x = self.ConvSet_2(x)\n",
    "        x = self.FC_Layers(x)\n",
    "        return x"
   ]
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train DataSet len: 3991\n",
      "Train DataSet Classes: 10\n",
      "Test DataSet len: 908\n",
      "Test DataSet Classes: 10\n"
     ]
    }
   ],
   "source": [
    "tra = transforms.Compose([\n",
    "    Select_Points(1024),\n",
    "    Normalize(),\n",
    "    ToTensor()\n",
    "])\n",
    "DataSet_Path = \"../input/modelnet10-princeton-3d-object-dataset/ModelNet10\"\n",
    "\n",
    "Train_Dataset = ModelNet_Dataset(DataSet_Path, \"train\", tra)\n",
    "Test_Dataset = ModelNet_Dataset(DataSet_Path, \"test\", tra)\n",
    "\n",
    "Train_loader = DataLoader(Train_Dataset, shuffle=True, num_workers=2, batch_size=64)\n",
    "Test_loader = DataLoader(Test_Dataset, shuffle=True, num_workers=2, batch_size=64)\n",
    "\n",
    "print('Train DataSet len:',len(Train_Dataset))\n",
    "print('Train DataSet Classes:',len(Train_Dataset.classes))\n",
    "print('Test DataSet len:',len(Test_Dataset))\n",
    "print('Test DataSet Classes:',len(Test_Dataset.classes))"
   ]
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    {
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     "text": [
      "0 Bath-Tub\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PointCloud = next(iter(Train_Dataset))\n",
    "print(PointCloud['Class'], 'Bath-Tub')\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(111, projection=\"3d\")\n",
    "ax.scatter(PointCloud['Data'][:, 0], PointCloud['Data'][:, 1], PointCloud['Data'][:, 2])\n",
    "# ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29d52385",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-02T13:48:16.725991Z",
     "iopub.status.busy": "2022-06-02T13:48:16.725773Z",
     "iopub.status.idle": "2022-06-02T13:48:27.374027Z",
     "shell.execute_reply": "2022-06-02T13:48:27.373287Z"
    },
    "papermill": {
     "duration": 10.672017,
     "end_time": "2022-06-02T13:48:27.376237",
     "exception": false,
     "start_time": "2022-06-02T13:48:16.704220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device:  cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "model = Point_Net(len(Train_Dataset.classes))\n",
    "model.load_state_dict(torch.load(\"../input/pointnet-trained-weights/PointNet_Model.pt\"))\n",
    "model = model.to(device=device)\n",
    "print('Device: ',device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "986e9dd7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-02T13:48:27.417188Z",
     "iopub.status.busy": "2022-06-02T13:48:27.416676Z",
     "iopub.status.idle": "2022-06-02T13:48:27.421136Z",
     "shell.execute_reply": "2022-06-02T13:48:27.420455Z"
    },
    "papermill": {
     "duration": 0.026652,
     "end_time": "2022-06-02T13:48:27.422722",
     "exception": false,
     "start_time": "2022-06-02T13:48:27.396070",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "learning_rate = 1e-4\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model.parameters(), lr= learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a02d83a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-02T13:48:27.462639Z",
     "iopub.status.busy": "2022-06-02T13:48:27.462456Z",
     "iopub.status.idle": "2022-06-02T17:02:10.179385Z",
     "shell.execute_reply": "2022-06-02T17:02:10.178323Z"
    },
    "papermill": {
     "duration": 11622.740559,
     "end_time": "2022-06-02T17:02:10.182566",
     "exception": false,
     "start_time": "2022-06-02T13:48:27.442007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10] :::: Loss: 0.3018\n",
      "Epoch [2/10] :::: Loss: 0.2693\n",
      "Epoch [3/10] :::: Loss: 0.2583\n",
      "Epoch [4/10] :::: Loss: 0.2455\n",
      "Epoch [5/10] :::: Loss: 0.2650\n",
      "Epoch [6/10] :::: Loss: 0.2376\n",
      "Epoch [7/10] :::: Loss: 0.2231\n",
      "Epoch [8/10] :::: Loss: 0.2183\n",
      "Epoch [9/10] :::: Loss: 0.2334\n",
      "Epoch [10/10] :::: Loss: 0.2196\n"
     ]
    }
   ],
   "source": [
    "num_epochs = 10\n",
    "for epoch in range(num_epochs):\n",
    "    loss_ep = 0\n",
    "    for data in Train_loader:\n",
    "        images = data['Data'].to(device=device).transpose(1, 2)\n",
    "        labels = data['Class'].to(device=device) \n",
    "        images = images.type(torch.cuda.FloatTensor)\n",
    "        optimizer.zero_grad()\n",
    "        scores = model(images)\n",
    "        loss = criterion(scores, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        loss_ep += loss.item()\n",
    "    print('Epoch [{}/{}] :::: Loss: {:.4f}'.format(epoch+1, num_epochs, loss_ep/len(Train_loader)))\n",
    "    torch.save(model.state_dict(), \"PointNet_Model.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "622acb9a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-02T17:02:10.233276Z",
     "iopub.status.busy": "2022-06-02T17:02:10.232630Z",
     "iopub.status.idle": "2022-06-02T17:02:17.213492Z",
     "shell.execute_reply": "2022-06-02T17:02:17.212590Z"
    },
    "papermill": {
     "duration": 7.007966,
     "end_time": "2022-06-02T17:02:17.216782",
     "exception": false,
     "start_time": "2022-06-02T17:02:10.208816",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), \"PointNet_Model.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c6ffc4e9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-02T17:02:17.281827Z",
     "iopub.status.busy": "2022-06-02T17:02:17.281608Z",
     "iopub.status.idle": "2022-06-02T17:02:19.871769Z",
     "shell.execute_reply": "2022-06-02T17:02:19.870993Z"
    },
    "papermill": {
     "duration": 2.615935,
     "end_time": "2022-06-02T17:02:19.873854",
     "exception": false,
     "start_time": "2022-06-02T17:02:17.257919",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Point_Net(\n",
       "  (T_Net3): T_Net(\n",
       "    (ConvBlock): Sequential(\n",
       "      (0): Conv1d(3, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (1): ReLU()\n",
       "      (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (4): ReLU()\n",
       "      (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (6): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (7): Conv1d(64, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (8): ReLU()\n",
       "      (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (10): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (11): ReLU()\n",
       "      (12): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (13): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (14): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (15): ReLU()\n",
       "      (16): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (17): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (18): ReLU()\n",
       "      (19): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (20): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (FC_Block): Sequential(\n",
       "      (0): Flatten(start_dim=1, end_dim=-1)\n",
       "      (1): Linear(in_features=16384, out_features=4096, bias=True)\n",
       "      (2): ReLU()\n",
       "      (3): Dropout(p=0.5, inplace=False)\n",
       "      (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "      (5): ReLU()\n",
       "      (6): Dropout(p=0.5, inplace=False)\n",
       "      (7): Linear(in_features=4096, out_features=9, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (ConvSet_1): Sequential(\n",
       "    (0): Conv1d(3, 32, kernel_size=(1,), stride=(1,))\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Conv1d(32, 64, kernel_size=(1,), stride=(1,))\n",
       "    (4): ReLU()\n",
       "    (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (6): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n",
       "    (7): ReLU()\n",
       "    (8): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (9): MaxPool1d(kernel_size=1, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (T_Net64): T_Net(\n",
       "    (ConvBlock): Sequential(\n",
       "      (0): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (1): ReLU()\n",
       "      (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (3): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (4): ReLU()\n",
       "      (5): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (6): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (7): Conv1d(64, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (8): ReLU()\n",
       "      (9): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (10): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (11): ReLU()\n",
       "      (12): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (13): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "      (14): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (15): ReLU()\n",
       "      (16): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (17): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,))\n",
       "      (18): ReLU()\n",
       "      (19): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (20): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
       "    )\n",
       "    (FC_Block): Sequential(\n",
       "      (0): Flatten(start_dim=1, end_dim=-1)\n",
       "      (1): Linear(in_features=16384, out_features=4096, bias=True)\n",
       "      (2): ReLU()\n",
       "      (3): Dropout(p=0.5, inplace=False)\n",
       "      (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "      (5): ReLU()\n",
       "      (6): Dropout(p=0.5, inplace=False)\n",
       "      (7): Linear(in_features=4096, out_features=4096, bias=True)\n",
       "    )\n",
       "  )\n",
       "  (ConvSet_2): Sequential(\n",
       "    (0): Conv1d(64, 64, kernel_size=(1,), stride=(1,))\n",
       "    (1): ReLU()\n",
       "    (2): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (3): Conv1d(64, 128, kernel_size=(1,), stride=(1,))\n",
       "    (4): ReLU()\n",
       "    (5): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (6): Conv1d(128, 128, kernel_size=(1,), stride=(1,))\n",
       "    (7): ReLU()\n",
       "    (8): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (9): Conv1d(128, 1024, kernel_size=(1,), stride=(1,))\n",
       "    (10): ReLU()\n",
       "    (11): BatchNorm1d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (12): MaxPool1d(kernel_size=1, stride=1, padding=0, dilation=1, ceil_mode=False)\n",
       "  )\n",
       "  (FC_Layers): Sequential(\n",
       "    (0): MaxPool1d(kernel_size=1024, stride=1024, padding=0, dilation=1, ceil_mode=False)\n",
       "    (1): Flatten(start_dim=1, end_dim=-1)\n",
       "    (2): Linear(in_features=1024, out_features=512, bias=True)\n",
       "    (3): ReLU()\n",
       "    (4): Dropout(p=0.5, inplace=False)\n",
       "    (5): Linear(in_features=512, out_features=256, bias=True)\n",
       "    (6): ReLU()\n",
       "    (7): Dropout(p=0.5, inplace=False)\n",
       "    (8): Linear(in_features=256, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Point_Net(len(Train_Dataset.classes))\n",
    "model.load_state_dict(torch.load(\"./PointNet_Model.pt\"))\n",
    "model = model.to(device=device)\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e45c42f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-06-02T17:02:19.922473Z",
     "iopub.status.busy": "2022-06-02T17:02:19.921978Z",
     "iopub.status.idle": "2022-06-02T17:06:00.538266Z",
     "shell.execute_reply": "2022-06-02T17:06:00.537418Z"
    },
    "papermill": {
     "duration": 220.667301,
     "end_time": "2022-06-02T17:06:00.564826",
     "exception": false,
     "start_time": "2022-06-02T17:02:19.897525",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the Model on the 908 Test Images: 87.33480176211454%\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    for data in Test_loader:\n",
    "        images = data['Data'].to(device=device).transpose(1, 2)\n",
    "        labels = data['Class'].to(device=device) \n",
    "        images = images.type(torch.cuda.FloatTensor)\n",
    "        outputs = model(images)\n",
    "        _, predicted = torch.max(outputs.data, 1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()           \n",
    "    print('Accuracy of the Model on the {} Test Images: {}%'.format(len(Test_Dataset), 100*correct /total))"
   ]
  }
 ],
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