notebooks/triposr-3d-reconstruction/triposr-3d-reconstruction.ipynb

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   "source": [
    "# TripoSR feedforward 3D reconstruction from a single image and OpenVINO\n",
    "\n",
    "[TripoSR](https://huggingface.co/spaces/stabilityai/TripoSR) is a state-of-the-art open-source model for fast feedforward 3D reconstruction from a single image, developed in collaboration between [Tripo AI](https://www.tripo3d.ai/) and [Stability AI](https://stability.ai/news/triposr-3d-generation).\n",
    "\n",
    "You can find [the source code on GitHub](https://github.com/VAST-AI-Research/TripoSR) and [demo on HuggingFace](https://huggingface.co/spaces/stabilityai/TripoSR). Also, you can read the paper [TripoSR: Fast 3D Object Reconstruction from a Single Image](https://arxiv.org/abs/2403.02151).\n",
    "\n",
    "![Teaser Video](https://raw.githubusercontent.com/VAST-AI-Research/TripoSR/main/figures/teaser800.gif)\n",
    "\n",
    "#### Table of contents:\n",
    "\n",
    "- [Prerequisites](#Prerequisites)\n",
    "- [Get the original model](#Get-the-original-model)\n",
    "- [Convert the model to OpenVINO IR](#Convert-the-model-to-OpenVINO-IR)\n",
    "- [Compiling models and prepare pipeline](#Compiling-models-and-prepare-pipeline)\n",
    "- [Interactive inference](#Interactive-inference)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b32875d0-9935-42df-ba9b-c60cb31d9eff",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cb7f8ed-aa2d-4124-a77d-42ed3324ccfd",
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%pip install -q \"gradio>=4.19\" \"torch==2.2.2\" rembg trimesh einops \"omegaconf>=2.3.0\" \"transformers>=4.35.0\" \"openvino>=2024.0.0\" --extra-index-url https://download.pytorch.org/whl/cpu\n",
    "%pip install -q \"git+https://github.com/tatsy/torchmcubes.git\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b78424b7-80e4-4470-9427-7286b9837566",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from pathlib import Path\n",
    "\n",
    "if not Path(\"TripoSR\").exists():\n",
    "    !git clone https://huggingface.co/spaces/stabilityai/TripoSR\n",
    "\n",
    "sys.path.append(\"TripoSR\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92e3f21e-9a4f-4fe4-aa16-016b4a118d5f",
   "metadata": {},
   "source": [
    "## Get the original model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7c990fa-0757-480c-8c42-d0abc50edc8e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from tsr.system import TSR\n",
    "\n",
    "\n",
    "model = TSR.from_pretrained(\n",
    "    \"stabilityai/TripoSR\",\n",
    "    config_name=\"config.yaml\",\n",
    "    weight_name=\"model.ckpt\",\n",
    ")\n",
    "model.renderer.set_chunk_size(131072)\n",
    "model.to(\"cpu\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f451a152-867c-46d0-b83f-475929ab0c92",
   "metadata": {},
   "source": [
    "### Convert the model to OpenVINO IR\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cdaa3200-f293-47d3-a7ab-b631e66e42dc",
   "metadata": {},
   "source": [
    "Define the conversion function for PyTorch modules. We use `ov.convert_model` function to obtain OpenVINO Intermediate Representation object and `ov.save_model` function to save it as XML file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d37dbcee-8c40-4cf6-a1ed-5b4c2358f4d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "import openvino as ov\n",
    "\n",
    "\n",
    "def convert(model: torch.nn.Module, xml_path: str, example_input):\n",
    "    xml_path = Path(xml_path)\n",
    "    if not xml_path.exists():\n",
    "        xml_path.parent.mkdir(parents=True, exist_ok=True)\n",
    "        with torch.no_grad():\n",
    "            converted_model = ov.convert_model(model, example_input=example_input)\n",
    "        ov.save_model(converted_model, xml_path, compress_to_fp16=False)\n",
    "\n",
    "        # cleanup memory\n",
    "        torch._C._jit_clear_class_registry()\n",
    "        torch.jit._recursive.concrete_type_store = torch.jit._recursive.ConcreteTypeStore()\n",
    "        torch.jit._state._clear_class_state()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "51d3ad7f-2c16-4967-811d-d68f788faa7c",
   "metadata": {},
   "source": [
    "The original model is a pipeline of several models. There are `image_tokenizer`, `tokenizer`, `backbone` and `post_processor`. `image_tokenizer` contains `ViTModel` that consists of `ViTPatchEmbeddings`, `ViTEncoder` and `ViTPooler`.  `tokenizer` is `Triplane1DTokenizer`, `backbone` is `Transformer1D`, `post_processor` is `TriplaneUpsampleNetwork`. Convert all internal models one by one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9456c3c-64dd-4109-968c-8f51d42b9876",
   "metadata": {},
   "outputs": [],
   "source": [
    "VIT_PATCH_EMBEDDINGS_OV_PATH = Path(\"models/vit_patch_embeddings_ir.xml\")\n",
    "\n",
    "\n",
    "class PatchEmbedingWrapper(torch.nn.Module):\n",
    "    def __init__(self, patch_embeddings):\n",
    "        super().__init__()\n",
    "        self.patch_embeddings = patch_embeddings\n",
    "\n",
    "    def forward(self, pixel_values, interpolate_pos_encoding=True):\n",
    "        outputs = self.patch_embeddings(pixel_values=pixel_values, interpolate_pos_encoding=True)\n",
    "        return outputs\n",
    "\n",
    "\n",
    "example_input = {\n",
    "    \"pixel_values\": torch.rand([1, 3, 512, 512], dtype=torch.float32),\n",
    "}\n",
    "\n",
    "convert(\n",
    "    PatchEmbedingWrapper(model.image_tokenizer.model.embeddings.patch_embeddings),\n",
    "    VIT_PATCH_EMBEDDINGS_OV_PATH,\n",
    "    example_input,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f31d0b34-9690-454c-b497-f2330d18bac4",
   "metadata": {},
   "outputs": [],
   "source": [
    "VIT_ENCODER_OV_PATH = Path(\"models/vit_encoder_ir.xml\")\n",
    "\n",
    "\n",
    "class EncoderWrapper(torch.nn.Module):\n",
    "    def __init__(self, encoder):\n",
    "        super().__init__()\n",
    "        self.encoder = encoder\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        hidden_states=None,\n",
    "        head_mask=None,\n",
    "        output_attentions=False,\n",
    "        output_hidden_states=False,\n",
    "        return_dict=False,\n",
    "    ):\n",
    "        outputs = self.encoder(\n",
    "            hidden_states=hidden_states,\n",
    "        )\n",
    "\n",
    "        return outputs.last_hidden_state\n",
    "\n",
    "\n",
    "example_input = {\n",
    "    \"hidden_states\": torch.rand([1, 1025, 768], dtype=torch.float32),\n",
    "}\n",
    "\n",
    "convert(\n",
    "    EncoderWrapper(model.image_tokenizer.model.encoder),\n",
    "    VIT_ENCODER_OV_PATH,\n",
    "    example_input,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07815ea4-f851-4c13-99b9-1b905ef998cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "VIT_POOLER_OV_PATH = Path(\"models/vit_pooler_ir.xml\")\n",
    "convert(\n",
    "    model.image_tokenizer.model.pooler,\n",
    "    VIT_POOLER_OV_PATH,\n",
    "    torch.rand([1, 1025, 768], dtype=torch.float32),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "465dc1db-5ce1-428f-bc90-740aa6d0dca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "TOKENIZER_OV_PATH = Path(\"models/tokenizer_ir.xml\")\n",
    "convert(model.tokenizer, TOKENIZER_OV_PATH, torch.tensor(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4b7cdbc5-ed39-49b4-922d-95603478209f",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_input = {\n",
    "    \"hidden_states\": torch.rand([1, 1024, 3072], dtype=torch.float32),\n",
    "    \"encoder_hidden_states\": torch.rand([1, 1025, 768], dtype=torch.float32),\n",
    "}\n",
    "\n",
    "BACKBONE_OV_PATH = Path(\"models/backbone_ir.xml\")\n",
    "convert(model.backbone, BACKBONE_OV_PATH, example_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b209191b-5566-48ad-adb4-0ceee5d4e469",
   "metadata": {},
   "outputs": [],
   "source": [
    "POST_PROCESSOR_OV_PATH = Path(\"models/post_processor_ir.xml\")\n",
    "convert(\n",
    "    model.post_processor,\n",
    "    POST_PROCESSOR_OV_PATH,\n",
    "    torch.rand([1, 3, 1024, 32, 32], dtype=torch.float32),\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "70b16770-acb7-4268-b135-4e3c0d7b1dd5",
   "metadata": {},
   "source": [
    "## Compiling models and prepare pipeline\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "221d88fe-7a91-4c39-a7e8-9e1f6eee0bc4",
   "metadata": {},
   "source": [
    "Select device from dropdown list for running inference using OpenVINO."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9324143f-02b5-4238-a173-4a649d9ab1d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "\n",
    "core = ov.Core()\n",
    "device = widgets.Dropdown(\n",
    "    options=core.available_devices + [\"AUTO\"],\n",
    "    value=\"AUTO\",\n",
    "    description=\"Device:\",\n",
    "    disabled=False,\n",
    ")\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "379a2cb2-6086-47d9-ae83-be7f74a5c14f",
   "metadata": {},
   "outputs": [],
   "source": [
    "compiled_vit_patch_embeddings = core.compile_model(VIT_PATCH_EMBEDDINGS_OV_PATH, device.value)\n",
    "compiled_vit_model_encoder = core.compile_model(VIT_ENCODER_OV_PATH, device.value)\n",
    "compiled_vit_model_pooler = core.compile_model(VIT_POOLER_OV_PATH, device.value)\n",
    "\n",
    "compiled_tokenizer = core.compile_model(TOKENIZER_OV_PATH, device.value)\n",
    "compiled_backbone = core.compile_model(BACKBONE_OV_PATH, device.value)\n",
    "compiled_post_processor = core.compile_model(POST_PROCESSOR_OV_PATH, device.value)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "beacf131-aa84-4a0b-9d62-4df35d9fe5a4",
   "metadata": {},
   "source": [
    "Let's create callable wrapper classes for compiled models to allow interaction with original `TSR` class. Note that all of wrapper classes return `torch.Tensor`s instead of `np.array`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f97d859-4e2d-4277-8816-7ca8d6c76778",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple\n",
    "\n",
    "\n",
    "class VitPatchEmdeddingsWrapper(torch.nn.Module):\n",
    "    def __init__(self, vit_patch_embeddings, model):\n",
    "        super().__init__()\n",
    "        self.vit_patch_embeddings = vit_patch_embeddings\n",
    "        self.projection = model.projection\n",
    "\n",
    "    def forward(self, pixel_values, interpolate_pos_encoding=False):\n",
    "        inputs = {\n",
    "            \"pixel_values\": pixel_values,\n",
    "        }\n",
    "        outs = self.vit_patch_embeddings(inputs)[0]\n",
    "\n",
    "        return torch.from_numpy(outs)\n",
    "\n",
    "\n",
    "class VitModelEncoderWrapper(torch.nn.Module):\n",
    "    def __init__(self, vit_model_encoder):\n",
    "        super().__init__()\n",
    "        self.vit_model_encoder = vit_model_encoder\n",
    "\n",
    "    def forward(\n",
    "        self,\n",
    "        hidden_states,\n",
    "        head_mask,\n",
    "        output_attentions=False,\n",
    "        output_hidden_states=False,\n",
    "        return_dict=False,\n",
    "    ):\n",
    "        inputs = {\n",
    "            \"hidden_states\": hidden_states.detach().numpy(),\n",
    "        }\n",
    "\n",
    "        outs = self.vit_model_encoder(inputs)\n",
    "        outputs = namedtuple(\"BaseModelOutput\", (\"last_hidden_state\", \"hidden_states\", \"attentions\"))\n",
    "\n",
    "        return outputs(torch.from_numpy(outs[0]), None, None)\n",
    "\n",
    "\n",
    "class VitModelPoolerWrapper(torch.nn.Module):\n",
    "    def __init__(self, vit_model_pooler):\n",
    "        super().__init__()\n",
    "        self.vit_model_pooler = vit_model_pooler\n",
    "\n",
    "    def forward(self, hidden_states):\n",
    "        outs = self.vit_model_pooler(hidden_states.detach().numpy())[0]\n",
    "\n",
    "        return torch.from_numpy(outs)\n",
    "\n",
    "\n",
    "class TokenizerWrapper(torch.nn.Module):\n",
    "    def __init__(self, tokenizer, model):\n",
    "        super().__init__()\n",
    "        self.tokenizer = tokenizer\n",
    "        self.detokenize = model.detokenize\n",
    "\n",
    "    def forward(self, batch_size):\n",
    "        outs = self.tokenizer(batch_size)[0]\n",
    "\n",
    "        return torch.from_numpy(outs)\n",
    "\n",
    "\n",
    "class BackboneWrapper(torch.nn.Module):\n",
    "    def __init__(self, backbone):\n",
    "        super().__init__()\n",
    "        self.backbone = backbone\n",
    "\n",
    "    def forward(self, hidden_states, encoder_hidden_states):\n",
    "        inputs = {\n",
    "            \"hidden_states\": hidden_states,\n",
    "            \"encoder_hidden_states\": encoder_hidden_states.detach().numpy(),\n",
    "        }\n",
    "\n",
    "        outs = self.backbone(inputs)[0]\n",
    "\n",
    "        return torch.from_numpy(outs)\n",
    "\n",
    "\n",
    "class PostProcessorWrapper(torch.nn.Module):\n",
    "    def __init__(self, post_processor):\n",
    "        super().__init__()\n",
    "        self.post_processor = post_processor\n",
    "\n",
    "    def forward(self, triplanes):\n",
    "        outs = self.post_processor(triplanes)[0]\n",
    "\n",
    "        return torch.from_numpy(outs)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6e18c61a-5a47-4ccb-a7dc-5b82caa1bf7d",
   "metadata": {},
   "source": [
    "Replace all models in the original model by wrappers instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04ada5f4-6a4a-418d-8227-379b76e3faf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.image_tokenizer.model.embeddings.patch_embeddings = VitPatchEmdeddingsWrapper(\n",
    "    compiled_vit_patch_embeddings,\n",
    "    model.image_tokenizer.model.embeddings.patch_embeddings,\n",
    ")\n",
    "model.image_tokenizer.model.encoder = VitModelEncoderWrapper(compiled_vit_model_encoder)\n",
    "model.image_tokenizer.model.pooler = VitModelPoolerWrapper(compiled_vit_model_pooler)\n",
    "\n",
    "model.tokenizer = TokenizerWrapper(compiled_tokenizer, model.tokenizer)\n",
    "model.backbone = BackboneWrapper(compiled_backbone)\n",
    "model.post_processor = PostProcessorWrapper(compiled_post_processor)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f032d9d5-5ee7-4fad-b1b8-e6b494bf840e",
   "metadata": {},
   "source": [
    "## Interactive inference\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "044bc9cf-3141-433d-a267-f5da84c9aa11",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "\n",
    "import gradio as gr\n",
    "import numpy as np\n",
    "import rembg\n",
    "from PIL import Image\n",
    "\n",
    "from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation\n",
    "\n",
    "\n",
    "rembg_session = rembg.new_session()\n",
    "\n",
    "\n",
    "def check_input_image(input_image):\n",
    "    if input_image is None:\n",
    "        raise gr.Error(\"No image uploaded!\")\n",
    "\n",
    "\n",
    "def preprocess(input_image, do_remove_background, foreground_ratio):\n",
    "    def fill_background(image):\n",
    "        image = np.array(image).astype(np.float32) / 255.0\n",
    "        image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5\n",
    "        image = Image.fromarray((image * 255.0).astype(np.uint8))\n",
    "        return image\n",
    "\n",
    "    if do_remove_background:\n",
    "        image = input_image.convert(\"RGB\")\n",
    "        image = remove_background(image, rembg_session)\n",
    "        image = resize_foreground(image, foreground_ratio)\n",
    "        image = fill_background(image)\n",
    "    else:\n",
    "        image = input_image\n",
    "        if image.mode == \"RGBA\":\n",
    "            image = fill_background(image)\n",
    "    return image\n",
    "\n",
    "\n",
    "def generate(image):\n",
    "    scene_codes = model(image, \"cpu\")  # the device is provided for the image processor\n",
    "    mesh = model.extract_mesh(scene_codes)[0]\n",
    "    mesh = to_gradio_3d_orientation(mesh)\n",
    "    mesh_path = tempfile.NamedTemporaryFile(suffix=\".obj\", delete=False)\n",
    "    mesh.export(mesh_path.name)\n",
    "    return mesh_path.name\n",
    "\n",
    "\n",
    "with gr.Blocks() as demo:\n",
    "    with gr.Row(variant=\"panel\"):\n",
    "        with gr.Column():\n",
    "            with gr.Row():\n",
    "                input_image = gr.Image(\n",
    "                    label=\"Input Image\",\n",
    "                    image_mode=\"RGBA\",\n",
    "                    sources=\"upload\",\n",
    "                    type=\"pil\",\n",
    "                    elem_id=\"content_image\",\n",
    "                )\n",
    "                processed_image = gr.Image(label=\"Processed Image\", interactive=False)\n",
    "            with gr.Row():\n",
    "                with gr.Group():\n",
    "                    do_remove_background = gr.Checkbox(label=\"Remove Background\", value=True)\n",
    "                    foreground_ratio = gr.Slider(\n",
    "                        label=\"Foreground Ratio\",\n",
    "                        minimum=0.5,\n",
    "                        maximum=1.0,\n",
    "                        value=0.85,\n",
    "                        step=0.05,\n",
    "                    )\n",
    "            with gr.Row():\n",
    "                submit = gr.Button(\"Generate\", elem_id=\"generate\", variant=\"primary\")\n",
    "        with gr.Column():\n",
    "            with gr.Tab(\"Model\"):\n",
    "                output_model = gr.Model3D(\n",
    "                    label=\"Output Model\",\n",
    "                    interactive=False,\n",
    "                )\n",
    "    with gr.Row(variant=\"panel\"):\n",
    "        gr.Examples(\n",
    "            examples=[os.path.join(\"TripoSR/examples\", img_name) for img_name in sorted(os.listdir(\"TripoSR/examples\"))],\n",
    "            inputs=[input_image],\n",
    "            outputs=[processed_image, output_model],\n",
    "            label=\"Examples\",\n",
    "            examples_per_page=20,\n",
    "        )\n",
    "    submit.click(fn=check_input_image, inputs=[input_image]).success(\n",
    "        fn=preprocess,\n",
    "        inputs=[input_image, do_remove_background, foreground_ratio],\n",
    "        outputs=[processed_image],\n",
    "    ).success(\n",
    "        fn=generate,\n",
    "        inputs=[processed_image],\n",
    "        outputs=[output_model],\n",
    "    )\n",
    "\n",
    "try:\n",
    "    demo.launch(debug=True, height=680)\n",
    "except Exception:\n",
    "    demo.queue().launch(share=True, debug=True, height=680)\n",
    "# if you are launching remotely, specify server_name and server_port\n",
    "# demo.launch(server_name='your server name', server_port='server port in int')\n",
    "# Read more in the docs: https://gradio.app/docs/"
   ]
  }
 ],
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