PointDreamer_demo.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yj0su8lBU_iB"
      },
      "source": [
        "# Welcome to the demo of PointDreamer!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bS6A_-wGU3V3",
        "outputId": "9b3f9f57-8267-4782-9149-015fa4056b92"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'PointDreamer'...\n",
            "remote: Enumerating objects: 294, done.\u001b[K\n",
            "remote: Counting objects: 100% (294/294), done.\u001b[K\n",
            "remote: Compressing objects: 100% (249/249), done.\u001b[K\n",
            "remote: Total 294 (delta 34), reused 289 (delta 32), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (294/294), 3.31 MiB | 15.89 MiB/s, done.\n",
            "Resolving deltas: 100% (34/34), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/YuQiao0303/PointDreamer.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pedDTa2HWrgU",
        "outputId": "dcbfaeef-18a0-4162-b2e7-76774f789576"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content\n",
            "['.config', 'PointDreamer', 'sample_data']\n",
            "/content/PointDreamer\n",
            "['.gitignore', 'dataset', 'utils', 'scripts', 'Readme.md', '.git', 'demo.py', 'requirements.txt', 'pointdreamer', 'assets', 'models', 'configs']\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "print(os.path.abspath('.'))\n",
        "print(os.listdir('.'))\n",
        "os.chdir('/content/PointDreamer')\n",
        "print(os.path.abspath('.'))\n",
        "print(os.listdir('.'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RUzyNqIVVLr3"
      },
      "source": [
        "# Install:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4rRKGl7cU-gV",
        "outputId": "9ce50248-35e1-4d78-c78c-42fdd3a82128"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://download.pytorch.org/whl/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.0+cu121)\n",
            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.18.0+cu121)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
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            "\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
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            "\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
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            "\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
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            "\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
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            "\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
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            "\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
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            "\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
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            "\u001b[?25hCollecting nvidia-nccl-cu12==2.20.5 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
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            "\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
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            "\u001b[?25hRequirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch)\n",
            "  Downloading https://download.pytorch.org/whl/cu121/nvidia_nvjitlink_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (19.8 MB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.25.2)\n",
            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
            "Requirement already satisfied: mpmath<1.4.0,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.1.105 nvidia-nvtx-cu12-12.1.105\n",
            "Collecting torch_cluster\n",
            "  Downloading torch_cluster-1.6.3.tar.gz (54 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_cluster) (1.11.4)\n",
            "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy->torch_cluster) (1.25.2)\n",
            "Building wheels for collected packages: torch_cluster\n"
          ]
        }
      ],
      "source": [
        "\n",
        "!pip install torch==2.0.0  torchvision==0.15.1 --index-url https://download.pytorch.org/whl/cu118\n",
        "#!pip install torch_cluster==1.6.3 --index-url https://data.pyg.org/whl/torch-2.0.0%2Bcu118 # not working\n",
        "!pip install kaolin==0.15.0 -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.0.0_cu118.html\n",
        "!pip install git+https://github.com/NVlabs/nvdiffrast/\n",
        "#!pip install https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_cluster-1.6.3%2Bpt20cu118-cp310-cp310-linux_x86_64.whl\n",
        "!pip install ninja xatlas gdown plyfile tensorboard scikit-image trimesh open3d munch pymcubes torch-geometric opencv-python\n",
        "# !pip install torch  torchvision  --index-url https://download.pytorch.org/whl/\n",
        "# !pip install torch_cluster\n",
        "!pip install pymeshlab kiui"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzkySkhVEkPI",
        "outputId": "af392926-ef71-4d50-cf1b-703dfb22f5d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "print(sys.version)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hhp-JCsLkFRF"
      },
      "source": [
        "You may need to install torch_cluster according to your python version."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tp9SGVuODRUa",
        "outputId": "b3012e4d-e636-455d-f8ed-b2a7961bc27a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting torch-cluster==1.6.3+pt20cu118\n",
            "  Downloading https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_cluster-1.6.3%2Bpt20cu118-cp310-cp310-linux_x86_64.whl (3.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch-cluster==1.6.3+pt20cu118) (1.11.4)\n",
            "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy->torch-cluster==1.6.3+pt20cu118) (1.25.2)\n",
            "Installing collected packages: torch-cluster\n",
            "Successfully installed torch-cluster-1.6.3+pt20cu118\n"
          ]
        }
      ],
      "source": [
        "#!pip cache purge\n",
        "\n",
        "!pip install https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_cluster-1.6.3%2Bpt20cu118-cp310-cp310-linux_x86_64.whl\n",
        "#!pip install https://data.pyg.org/whl/torch-2.0.0%2Bcu118/torch_cluster-1.6.3%2Bpt20cu118-cp311-cp311-linux_x86_64.whl\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YB3RaipFVp9I"
      },
      "source": [
        "## Download Pretrained weights of POCO & guided diffusion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U6vREmIQVsXw",
        "outputId": "1dfb6dfe-2871-4d1e-b37d-d566a91f0832"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-06-19 10:22:57--  https://github.com/valeoai/POCO/releases/download/v0.0.0/ShapeNet_3k.zip\n",
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            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
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            "Saving to: ‘ShapeNet_3k.zip’\n",
            "\n",
            "ShapeNet_3k.zip     100%[===================>] 114.71M   317MB/s    in 0.4s    \n",
            "\n",
            "2024-06-19 10:22:58 (317 MB/s) - ‘ShapeNet_3k.zip’ saved [120285490/120285490]\n",
            "\n",
            "Archive:  ShapeNet_3k.zip\n",
            "   creating: ShapeNet_None_FKAConv_InterpAttentionKHeadsNet_None/\n",
            "  inflating: ShapeNet_None_FKAConv_InterpAttentionKHeadsNet_None/config.yaml  \n",
            "  inflating: ShapeNet_None_FKAConv_InterpAttentionKHeadsNet_None/checkpoint.pth  \n"
          ]
        }
      ],
      "source": [
        "!wget https://github.com/valeoai/POCO/releases/download/v0.0.0/ShapeNet_3k.zip\n",
        "!unzip ShapeNet_3k.zip\n",
        "!mv ShapeNet_None_FKAConv_InterpAttentionKHeadsNet_None/checkpoint.pth models/POCO/\n",
        "!rm -r ShapeNet_None_FKAConv_InterpAttentionKHeadsNet_None\n",
        "!rm ShapeNet_3k.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7EUnXv1Vztd",
        "outputId": "d8a89e5b-b5cc-4e4a-d5eb-22d8849c0832"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2024-06-19 10:23:06--  https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt\n",
            "Resolving openaipublic.blob.core.windows.net (openaipublic.blob.core.windows.net)... 20.60.179.33\n",
            "Connecting to openaipublic.blob.core.windows.net (openaipublic.blob.core.windows.net)|20.60.179.33|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 2211383297 (2.1G) [application/octet-stream]\n",
            "Saving to: ‘256x256_diffusion_uncond.pt’\n",
            "\n",
            "256x256_diffusion_u 100%[===================>]   2.06G  31.1MB/s    in 72s     \n",
            "\n",
            "2024-06-19 10:24:18 (29.3 MB/s) - ‘256x256_diffusion_uncond.pt’ saved [2211383297/2211383297]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://openaipublic.blob.core.windows.net/diffusion/jul-2021/256x256_diffusion_uncond.pt\n",
        "!mv 256x256_diffusion_uncond.pt models/DDNM/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOx1PxzHWDxX"
      },
      "source": [
        "# Now run the demo!\n",
        "The first time running can be slower for installing some plug-ins. But afterwards it would be fine."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_m_qWL1WCvOD",
        "outputId": "c79062d2-515d-4cd5-9387-010e670694da"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "start import\n",
            "finish import\n",
            "logfile output/2024.10.06.03.35.12_log.log\n",
            "2024-10-06 11:35:12,155 -  Loading inpainter...\n",
            "2024-10-06 11:35:21,793 -  inpainter loaded\n",
            "2024-10-06 11:35:21,813 -  Loading POCO...\n",
            "2024-10-06 11:35:21,909 -  InterpNet - Simple - K=64\n",
            "2024-10-06 11:35:21,911 -  Network -- backbone -- 12783956 parameters\n",
            "2024-10-06 11:35:21,911 -  Network -- projection -- 6498 parameters\n",
            "  0%|                                                     | 0/1 [00:00<?, ?it/s]2024-10-06 11:35:22,125 -  Start Recon dataset/demo_data/clock.ply...\n",
            "2024-10-06 11:35:22,153 -  Get Geometry by POCO...\n",
            "2024-10-06 11:35:22,159 -  Submanifold sampling\n",
            "2024-10-06 11:35:40,627 -  18.468014240264893\n",
            "2024-10-06 11:35:40,631 -  Get Geometry time: 18.479106903076172 s by POCO\n",
            "2024-10-06 11:35:40,632 -  UV Unwrapping by xatlas...\n",
            "2024-10-06 11:35:40,633 -  Conducting UV Unwrapping...\n",
            "2024-10-06 11:35:42,509 -  xatlas time: 1.8767385482788086 s\n",
            "2024-10-06 11:35:42,509 -  Generate texture by PointDreamer...\n",
            "/opt/conda/envs/get3d/lib/python3.8/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n",
            "0 / 8 inpainting time 14.78046441078186 s\n",
            "1 / 8 inpainting time 10.507638692855835 s\n",
            "2 / 8 inpainting time 10.52436089515686 s\n",
            "3 / 8 inpainting time 10.748851537704468 s\n",
            "4 / 8 inpainting time 10.708950757980347 s\n",
            "5 / 8 inpainting time 10.67240834236145 s\n",
            "6 / 8 inpainting time 10.738286256790161 s\n",
            "7 / 8 inpainting time 10.764195919036865 s\n",
            "inpainting time 89.44515681266785 s for 8 images\n",
            "\n",
            "10it [00:01,  7.32it/s]\n",
            "2024-10-06 11:37:26,234 -  generate texture time: 103.72437191009521 s\n",
            "save output/clock_default/models/model_normalized.mtl\n",
            "img.shape (1024, 1024, 3)\n",
            "2024-10-06 11:37:27,474 -  total time: 125.32152104377747 s\n",
            "100%|████████████████████████████████████████████| 1/1 [02:05<00:00, 125.36s/it]\n"
          ]
        }
      ],
      "source": [
        "!python demo.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLaXHXpCXa9b"
      },
      "source": [
        "The reconstructed textured mesh and corresponding material and texture files are saved at 'output/clock_default/models/'. Download all of them and put them in the same folder. And then you can open the '.obj' file by Meshlab, Blender, or other tools!"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.16"
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  "nbformat": 4,
  "nbformat_minor": 0
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