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Commit c49ebad2 authored by Jeremy Reizenstein's avatar Jeremy Reizenstein Committed by Facebook GitHub Bot
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tutorial fix: add visdom 

Summary: need to pip install visdom in new volumes tutorial.

Reviewed By: kjchalup

Differential Revision: D38501905

fbshipit-source-id: 534bf097e41f05b3389e9420e6dd2b61a4517861
parent 1cd0cbff
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docs/tutorials/implicitron_volumes.ipynb
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"# Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved."
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"# A simple model using Implicitron\n",
......@@ -73,36 +32,36 @@
"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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"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",
......@@ -129,48 +88,48 @@
" !tar xzf 1.10.0.tar.gz\n",
" os.environ[\"CUB_HOME\"] = os.getcwd() + \"/cub-1.10.0\"\n",
" !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
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"Ensure omegaconf is installed. If not, run this cell. (It should not be necessary to restart the runtime.)"
],
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"Ensure omegaconf and visdom are installed. If not, run this cell. (It should not be necessary to restart the runtime.)"
]
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"!pip install omegaconf"
],
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"!pip install omegaconf visdom"
]
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"source": [
"import logging\n",
"from typing import Tuple\n",
......@@ -193,56 +152,55 @@
"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",
......@@ -250,50 +208,49 @@
"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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"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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"If we want to instantiate one of Implicitron's configurable objects, such as `RenderedMeshDatasetMapProvider`, without using the OmegaConf initialisation (get_default_args), we need to call `expand_args_fields` on the class first."
......@@ -301,17 +258,19 @@
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"expand_args_fields(RenderedMeshDatasetMapProvider)\n",
"cow_provider = RenderedMeshDatasetMapProvider(\n",
......@@ -319,83 +278,82 @@
" 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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"# 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",
......@@ -409,23 +367,24 @@
"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",
......@@ -463,39 +422,38 @@
"\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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" 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",
......@@ -521,82 +479,79 @@
" )\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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"outputs": []
" 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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"# 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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"## 4. train the model "
......@@ -604,58 +559,60 @@
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"train_data_collated = [FrameData.collate([frame.to(device)]) for frame in dataset_map.train]"
],
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]
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"gm.train()\n",
"optimizer = torch.optim.Adam(gm.parameters(), lr=0.1)"
],
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]
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"source": [
"iterator = tqdm.tqdm(range(2000))\n",
"for n_batch in iterator:\n",
......@@ -667,43 +624,43 @@
" if n_batch % 100 == 0:\n",
" iterator.set_postfix_str(f\"loss: {float(out['objective']):.5f}\")\n",
" optimizer.step()"
],
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"outputs": []
]
},
{
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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",
......@@ -731,21 +688,20 @@
" masks.append(mask)\n",
" expected.append(expd)\n",
" masks_expected.append(mask_expected)"
],
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]
},
{
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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",
......@@ -788,23 +744,24 @@
" ...\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",
......@@ -821,47 +778,46 @@
" 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",
""
],
"execution_count": null,
"outputs": []
"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)"
],
"execution_count": null,
"outputs": []
]
},
{
"cell_type": "code",
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"source": [
"plt.ioff()\n",
"fig, ax = plt.subplots(figsize=(3,3))\n",
......@@ -869,51 +825,89 @@
"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",
""
],
"execution_count": null,
"outputs": []
"ani_html = ani.to_jshtml()\n"
]
},
{
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"source": [
"HTML(ani_html)"
],
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]
},
{
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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",
""
],
"execution_count": null,
"outputs": []
"# gm._implicit_functions[0]._fn.color.fill_(9.0)\n"
]
}
]
],
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