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"source": [
"# Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved."
]
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"source": [
"# A simple model using Implicitron\n",
"\n",
"In this demo, we use the VolumeRenderer from PyTorch3D as a custom implicit function in Implicitron. We will see\n",
"* some of the main objects in Implicitron\n",
"* how to plug in a custom part of a model"
]
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"source": [
"## 0. Install and import modules\n",
"\n",
"Ensure `torch` and `torchvision` are installed. If `pytorch3d` is not installed, install it using the following cell:\n"
]
},
{
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"source": [
"import os\n",
"import sys\n",
"import torch\n",
"import subprocess\n",
"need_pytorch3d=False\n",
"try:\n",
" import pytorch3d\n",
"except ModuleNotFoundError:\n",
" need_pytorch3d=True\n",
"if need_pytorch3d:\n",
" pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
" version_str=\"\".join([\n",
" f\"py3{sys.version_info.minor}_cu\",\n",
" torch.version.cuda.replace(\".\",\"\"),\n",
" f\"_pyt{pyt_version_str}\"\n",
" ])\n",
" !pip install fvcore iopath\n",
" if sys.platform.startswith(\"linux\"):\n",
" print(\"Trying to install wheel for PyTorch3D\")\n",
" !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
" pip_list = !pip freeze\n",
" need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for i in pip_list)\n",
" if need_pytorch3d:\n",
" print(f\"failed to find/install wheel for {version_str}\")\n",
"if need_pytorch3d:\n",
" print(\"Installing PyTorch3D from source\")\n",
" !pip install ninja\n",
" !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
]
},
{
"attachments": {},
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"source": [
"Ensure omegaconf and visdom are installed. If not, run this cell. (It should not be necessary to restart the runtime.)"
]
},
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"source": [
"!pip install omegaconf visdom"
]
},
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"source": [
"import logging\n",
"from typing import Tuple\n",
"\n",
"import matplotlib.animation as animation\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import torch\n",
"import tqdm\n",
"from IPython.display import HTML\n",
"from omegaconf import OmegaConf\n",
"from PIL import Image\n",
"from pytorch3d.implicitron.dataset.dataset_base import FrameData\n",
"from pytorch3d.implicitron.dataset.rendered_mesh_dataset_map_provider import RenderedMeshDatasetMapProvider\n",
"from pytorch3d.implicitron.models.generic_model import GenericModel\n",
"from pytorch3d.implicitron.models.implicit_function.base import ImplicitFunctionBase, ImplicitronRayBundle\n",
"from pytorch3d.implicitron.models.renderer.base import EvaluationMode\n",
"from pytorch3d.implicitron.tools.config import get_default_args, registry, remove_unused_components\n",
"from pytorch3d.renderer.implicit.renderer import VolumeSampler\n",
"from pytorch3d.structures import Volumes\n",
"from pytorch3d.vis.plotly_vis import plot_batch_individually, plot_scene"
]
},
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"output_resolution = 80"
]
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"torch.set_printoptions(sci_mode=False)"
]
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"source": [
"## 1. Load renders of a mesh (the cow mesh) as a dataset\n",
"\n",
"A dataset's train, val and test parts in Implicitron are represented as a `dataset_map`, and provided by an implementation of `DatasetMapProvider`. \n",
"`RenderedMeshDatasetMapProvider` is one which generates a single-scene dataset with only a train component by taking a mesh and rendering it.\n",
"We use it with the cow mesh."
]
},
{
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"source": [
"If running this notebook using **Google Colab**, run the following cell to fetch the mesh obj and texture files and save it at the path data/cow_mesh.\n",
"If running locally, the data is already available at the correct path."
]
},
{
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"source": [
"!mkdir -p data/cow_mesh\n",
"!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.obj\n",
"!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow.mtl\n",
"!wget -P data/cow_mesh https://dl.fbaipublicfiles.com/pytorch3d/data/cow_mesh/cow_texture.png"
]
},
{
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"source": [
"cow_provider = RenderedMeshDatasetMapProvider(\n",
" data_file=\"data/cow_mesh/cow.obj\",\n",
" use_point_light=False,\n",
" resolution=output_resolution,\n",
")"
]
},
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"source": [
"dataset_map = cow_provider.get_dataset_map()\n",
"tr_cameras = [training_frame.camera for training_frame in dataset_map.train]"
]
},
{
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"source": [
"# The cameras are all in the XZ plane, in a circle about 2.7 from the origin\n",
"centers = torch.cat([i.get_camera_center() for i in tr_cameras])\n",
"print(centers.min(0).values)\n",
"print(centers.max(0).values)"
]
},
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"source": [
"# visualization of the cameras\n",
"plot = plot_scene({\"k\": {i: camera for i, camera in enumerate(tr_cameras)}}, camera_scale=0.25)\n",
"plot.layout.scene.aspectmode = \"data\"\n",
"plot"
]
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"source": [
"## 2. Custom implicit function 🧊\n",
"\n",
"At the core of neural rendering methods are functions of spatial coordinates called implicit functions, which are used in some kind of rendering process.\n",
"(Often those functions can additionally take other data as well, such as view direction.)\n",
"A common rendering process is ray marching over densities and colors provided by an implicit function.\n",
"In our case, taking samples from a 3D volume grid is a very simple function of spatial coordinates. \n",
"\n",
"Here we define our own implicit function, which uses PyTorch3D's existing functionality for sampling from a volume grid.\n",
"We do this by subclassing `ImplicitFunctionBase`.\n",
"We need to register our subclass with a special decorator.\n",
"We use Python's dataclass annotations for configuring the module."
]
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"source": [
"@registry.register\n",
"class MyVolumes(ImplicitFunctionBase, torch.nn.Module):\n",
" grid_resolution: int = 50 # common HWD of volumes, the number of voxels in each direction\n",
" extent: float = 1.0 # In world coordinates, the volume occupies is [-extent, extent] along each axis\n",
"\n",
" def __post_init__(self):\n",
" # We have to call this explicitly if there are other base classes like Module\n",
" super().__init__()\n",
"\n",
" # We define parameters like other torch.nn.Module objects.\n",
" # In this case, both our parameter tensors are trainable; they govern the contents of the volume grid.\n",
" density = torch.full((self.grid_resolution, self.grid_resolution, self.grid_resolution), -2.0)\n",
" self.density = torch.nn.Parameter(density)\n",
" color = torch.full((3, self.grid_resolution, self.grid_resolution, self.grid_resolution), 0.0)\n",
" self.color = torch.nn.Parameter(color)\n",
" self.density_activation = torch.nn.Softplus()\n",
"\n",
" def forward(\n",
" self,\n",
" ray_bundle: ImplicitronRayBundle,\n",
" fun_viewpool=None,\n",
" global_code=None,\n",
" ):\n",
" densities = self.density_activation(self.density[None, None])\n",
" voxel_size = 2.0 * float(self.extent) / self.grid_resolution\n",
" features = self.color.sigmoid()[None]\n",
"\n",
" # Like other PyTorch3D structures, the actual Volumes object should only exist as long\n",
" # as one iteration of training. It is local to this function.\n",
"\n",
" volume = Volumes(densities=densities, features=features, voxel_size=voxel_size)\n",
" sampler = VolumeSampler(volumes=volume)\n",
" densities, features = sampler(ray_bundle)\n",
"\n",
" # When an implicit function is used for raymarching, i.e. for MultiPassEmissionAbsorptionRenderer,\n",
" # it must return (densities, features, an auxiliary tuple)\n",
" return densities, features, {}\n"
]
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"source": [
"## 3. Construct the model object.\n",
"\n",
"The main model object in PyTorch3D is `GenericModel`, which has pluggable components for the major steps, including the renderer and the implicit function(s).\n",
"There are two ways to construct it which are equivalent here."
]
},
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"CONSTRUCT_MODEL_FROM_CONFIG = True\n",
"if CONSTRUCT_MODEL_FROM_CONFIG:\n",
" # Via a DictConfig - this is how our training loop with hydra works\n",
" cfg = get_default_args(GenericModel)\n",
" cfg.implicit_function_class_type = \"MyVolumes\"\n",
" cfg.render_image_height=output_resolution\n",
" cfg.render_image_width=output_resolution\n",
" cfg.loss_weights={\"loss_rgb_huber\": 1.0}\n",
" cfg.tqdm_trigger_threshold=19000\n",
" cfg.raysampler_AdaptiveRaySampler_args.scene_extent= 4.0\n",
" gm = GenericModel(**cfg)\n",
"else:\n",
" # constructing GenericModel directly\n",
" gm = GenericModel(\n",
" implicit_function_class_type=\"MyVolumes\",\n",
" render_image_height=output_resolution,\n",
" render_image_width=output_resolution,\n",
" loss_weights={\"loss_rgb_huber\": 1.0},\n",
" tqdm_trigger_threshold=19000,\n",
" raysampler_AdaptiveRaySampler_args = {\"scene_extent\": 4.0}\n",
" )\n",
"\n",
" # In this case we can get the equivalent DictConfig cfg object to the way gm is configured as follows\n",
" cfg = OmegaConf.structured(gm)\n"
]
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" The default renderer is an emission-absorbtion raymarcher. We keep that default."
]
},
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"source": [
"# We can display the configuration in use as follows.\n",
"remove_unused_components(cfg)\n",
"yaml = OmegaConf.to_yaml(cfg, sort_keys=False)\n",
"%page -r yaml"
]
},
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"source": [
"device = torch.device(\"cuda:0\")\n",
"gm.to(device)\n",
"assert next(gm.parameters()).is_cuda"
]
},
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"source": [
"## 4. train the model "
]
},
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"source": [
"train_data_collated = [FrameData.collate([frame.to(device)]) for frame in dataset_map.train]"
]
},
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"gm.train()\n",
"optimizer = torch.optim.Adam(gm.parameters(), lr=0.1)"
]
},
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"source": [
"iterator = tqdm.tqdm(range(2000))\n",
"for n_batch in iterator:\n",
" optimizer.zero_grad()\n",
"\n",
" frame = train_data_collated[n_batch % len(dataset_map.train)]\n",
" out = gm(**frame, evaluation_mode=EvaluationMode.TRAINING)\n",
" out[\"objective\"].backward()\n",
" if n_batch % 100 == 0:\n",
" iterator.set_postfix_str(f\"loss: {float(out['objective']):.5f}\")\n",
" optimizer.step()"
]
},
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"attachments": {},
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"source": [
"## 5. Evaluate the module\n",
"\n",
"We generate complete images from all the viewpoints to see how they look."
]
},
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"source": [
"def to_numpy_image(image):\n",
" # Takes an image of shape (C, H, W) in [0,1], where C=3 or 1\n",
" # to a numpy uint image of shape (H, W, 3)\n",
" return (image * 255).to(torch.uint8).permute(1, 2, 0).detach().cpu().expand(-1, -1, 3).numpy()\n",
"def resize_image(image):\n",
" # Takes images of shape (B, C, H, W) to (B, C, output_resolution, output_resolution)\n",
" return torch.nn.functional.interpolate(image, size=(output_resolution, output_resolution))\n",
"\n",
"gm.eval()\n",
"images = []\n",
"expected = []\n",
"masks = []\n",
"masks_expected = []\n",
"for frame in tqdm.tqdm(train_data_collated):\n",
" with torch.no_grad():\n",
" out = gm(**frame, evaluation_mode=EvaluationMode.EVALUATION)\n",
"\n",
" image_rgb = to_numpy_image(out[\"images_render\"][0])\n",
" mask = to_numpy_image(out[\"masks_render\"][0])\n",
" expd = to_numpy_image(resize_image(frame.image_rgb)[0])\n",
" mask_expected = to_numpy_image(resize_image(frame.fg_probability)[0])\n",
"\n",
" images.append(image_rgb)\n",
" masks.append(mask)\n",
" expected.append(expd)\n",
" masks_expected.append(mask_expected)"
]
},
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"source": [
"We draw a grid showing predicted image and expected image, followed by predicted mask and expected mask, from each viewpoint. \n",
"This is a grid of four rows of images, wrapped in to several large rows, i.e..\n",
"<small><center>\n",
"```\n",
"┌────────┬────────┐ ┌────────┐\n",
"│pred │pred │ │pred │\n",
"│image │image │ │image │\n",
"│1 │2 │ │n │\n",
"├────────┼────────┤ ├────────┤\n",
"│expected│expected│ │expected│\n",
"│image │image │ ... │image │\n",
"│1 │2 │ │n │\n",
"├────────┼────────┤ ├────────┤\n",
"│pred │pred │ │pred │\n",
"│mask │mask │ │mask │\n",
"│1 │2 │ │n │\n",
"├────────┼────────┤ ├────────┤\n",
"│expected│expected│ │expected│\n",
"│mask │mask │ │mask │\n",
"│1 │2 │ │n │\n",
"├────────┼────────┤ ├────────┤\n",
"│pred │pred │ │pred │\n",
"│image │image │ │image │\n",
"│n+1 │n+1 │ │2n │\n",
"├────────┼────────┤ ├────────┤\n",
"│expected│expected│ │expected│\n",
"│image │image │ ... │image │\n",
"│n+1 │n+2 │ │2n │\n",
"├────────┼────────┤ ├────────┤\n",
"│pred │pred │ │pred │\n",
"│mask │mask │ │mask │\n",
"│n+1 │n+2 │ │2n │\n",
"├────────┼────────┤ ├────────┤\n",
"│expected│expected│ │expected│\n",
"│mask │mask │ │mask │\n",
"│n+1 │n+2 │ │2n │\n",
"└────────┴────────┘ └────────┘\n",
" ...\n",
"```\n",
"</center></small>"
]
},
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"source": [
"images_to_display = [images.copy(), expected.copy(), masks.copy(), masks_expected.copy()]\n",
"n_rows = 4\n",
"n_images = len(images)\n",
"blank_image = images[0] * 0\n",
"n_per_row = 1+(n_images-1)//n_rows\n",
"for _ in range(n_per_row*n_rows - n_images):\n",
" for group in images_to_display:\n",
" group.append(blank_image)\n",
"\n",
"images_to_display_listed = [[[i] for i in j] for j in images_to_display]\n",
"split = []\n",
"for row in range(n_rows):\n",
" for group in images_to_display_listed:\n",
" split.append(group[row*n_per_row:(row+1)*n_per_row]) \n",
"\n",
"Image.fromarray(np.block(split))\n"
]
},
{
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"source": [
"# Print the maximum channel intensity in the first image.\n",
"print(images[1].max()/255)"
]
},
{
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"source": [
"plt.ioff()\n",
"fig, ax = plt.subplots(figsize=(3,3))\n",
"\n",
"ax.grid(None)\n",
"ims = [[ax.imshow(im, animated=True)] for im in images]\n",
"ani = animation.ArtistAnimation(fig, ims, interval=80, blit=True)\n",
"ani_html = ani.to_jshtml()\n"
]
},
{
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"executionStartTime": 1659621409620,
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"requestMsgId": "b6843506-c5fa-4508-80fc-8ecae51a934a",
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"outputs": [],
"source": [
"HTML(ani_html)"
]
},
{
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"metadata": {
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"source": [
"# If you want to see the output of the model with the volume forced to opaque white, run this and re-evaluate\n",
"# with torch.no_grad():\n",
"# gm._implicit_functions[0]._fn.density.fill_(9.0)\n",
"# gm._implicit_functions[0]._fn.color.fill_(9.0)\n"
]
}
],
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