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pulsar integration.

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Summary:
This diff integrates the pulsar renderer source code into PyTorch3D as an alternative backend for the PyTorch3D point renderer. This diff is the first of a series of three diffs to complete that migration and focuses on the packaging and integration of the source code.

For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder `docs/examples`.

Tasks addressed in the following diffs:
* Add the PyTorch3D interface,
* Add notebook examples and documentation (or adapt the existing ones to feature both interfaces).

Reviewed By: nikhilaravi

Differential Revision: D23947736

fbshipit-source-id: a5e77b53e6750334db22aefa89b4c079cda1b443
This commit is contained in:
Christoph Lassner
2020-11-03 13:05:02 -08:00
committed by Facebook GitHub Bot
parent d565032399
commit b19fe1de2f
137 changed files with 10055 additions and 37 deletions
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pytorch3d/renderer/points/pulsar/__init__.py Normal file
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from .renderer import Renderer # noqa: F401

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pytorch3d/renderer/points/pulsar/renderer.py Normal file
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
"""pulsar renderer PyTorch integration.
Proper Python support for pytorch requires creating a torch.autograd.function
(independent of whether this is being done within the C++ module). This is done
here and a torch.nn.Module is exposed for the use in more complex models.
"""
import logging
import math
import warnings
from typing import Optional, Tuple, Union
import torch
# pyre-fixme[21]: Could not find a name `_C` defined in module `pytorch3d`.
from pytorch3d import _C
from pytorch3d.transforms import axis_angle_to_matrix, rotation_6d_to_matrix
LOGGER = logging.getLogger(__name__)
GAMMA_WARNING_EMITTED = False
AXANGLE_WARNING_EMITTED = False
class _Render(torch.autograd.Function):
"""
Differentiable rendering function for the Pulsar renderer.
Usually this will be used through the `Renderer` module, which takes care of
setting up the buffers and putting them on the correct device. If you use
the function directly, you will have to do this manually.
The steps for this are two-fold: first, you need to create a native Renderer
object to provide the required buffers. This is the `native_renderer` parameter
for this function. You can create it by creating a `pytorch3d._C.PulsarRenderer`
object (with parameters for width, height and maximum number of balls it should
be able to render). This object by default resides on the CPU. If you want to
shift the buffers to a different device, just assign an empty tensor on the target
device to its property `device_tracker`.
To convert camera parameters from a more convenient representation to the
required vectors as in this function, you can use the static
function `pytorch3d.renderer.points.pulsar.Renderer._transform_cam_params`.
Args:
* ctx: Pytorch context.
* vert_pos: vertex positions. [Bx]Nx3 tensor of positions in 3D space.
* vert_col: vertex colors. [Bx]NxK tensor of channels.
* vert_rad: vertex radii. [Bx]N tensor of radiuses, >0.
* cam_pos: camera position(s). [Bx]3 tensor in 3D coordinates.
* pixel_0_0_center: [Bx]3 tensor center(s) of the upper left pixel(s) in
world coordinates.
* pixel_vec_x: [Bx]3 tensor from one pixel center to the next in image x
direction in world coordinates.
* pixel_vec_y: [Bx]3 tensor from one pixel center to the next in image y
direction in world coordinates.
* focal_length: [Bx]1 tensor of focal lengths in world coordinates.
* principal_point_offsets: [Bx]2 tensor of principal point offsets in pixels.
* gamma: sphere transparency in [1.,1E-5], with 1 being mostly transparent.
[Bx]1.
* max_depth: maximum depth for spheres to render. Set this as tighly
as possible to have good numerical accuracy for gradients.
* native_renderer: a `pytorch3d._C.PulsarRenderer` object.
* min_depth: a float with the minimum depth a sphere must have to be renderer.
Must be 0. or > max(focal_length).
* bg_col: K tensor with a background color to use or None (uses all ones).
* opacity: [Bx]N tensor of opacity values in [0., 1.] or None (uses all ones).
* percent_allowed_difference: a float in [0., 1.[ with the maximum allowed
difference in color space. This is used to speed up the
computation. Default: 0.01.
* max_n_hits: a hard limit on the number of hits per ray. Default: max int.
* mode: render mode in {0, 1}. 0: render an image; 1: render the hit map.
* return_forward_info: whether to return a second map. This second map contains
13 channels: first channel contains sm_m (the maximum exponent factor
observed), the second sm_d (the normalization denominator, the sum of all
coefficients), the third the maximum closest possible intersection for a
hit. The following channels alternate with the float encoded integer index
of a sphere and its weight. They are the five spheres with the highest
color contribution to this pixel color, ordered descending.
Returns:
* image: [Bx]HxWxK float tensor with the resulting image.
* forw_info: [Bx]HxWx13 float forward information as described above,
if enabled.
"""
@staticmethod
def forward(
ctx,
vert_pos,
vert_col,
vert_rad,
cam_pos,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
focal_length,
principal_point_offsets,
gamma,
max_depth,
native_renderer,
min_depth=0.0,
bg_col=None,
opacity=None,
percent_allowed_difference=0.01,
max_n_hits=_C.MAX_UINT,
mode=0,
return_forward_info=False,
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
if mode != 0:
assert not return_forward_info, (
"You are using a non-standard rendering mode. This does "
"not provide gradients, and also no `forward_info`. Please "
"set `return_forward_info` to `False`."
)
ctx.gamma = gamma
ctx.max_depth = max_depth
ctx.min_depth = min_depth
ctx.percent_allowed_difference = percent_allowed_difference
ctx.max_n_hits = max_n_hits
ctx.mode = mode
ctx.native_renderer = native_renderer
image, info = ctx.native_renderer.forward(
vert_pos,
vert_col,
vert_rad,
cam_pos,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
focal_length,
principal_point_offsets,
gamma,
max_depth,
min_depth,
bg_col,
opacity,
percent_allowed_difference,
max_n_hits,
mode,
)
if mode != 0:
# Backprop not possible!
info = None
# Prepare for backprop.
ctx.save_for_backward(
vert_pos,
vert_col,
vert_rad,
cam_pos,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
focal_length,
principal_point_offsets,
bg_col,
opacity,
image,
info,
)
if return_forward_info:
return image, info
else:
return image
@staticmethod
def backward(ctx, grad_im, *args):
global GAMMA_WARNING_EMITTED
(
vert_pos,
vert_col,
vert_rad,
cam_pos,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
focal_length,
principal_point_offsets,
bg_col,
opacity,
image,
info,
) = ctx.saved_tensors
if (
(
ctx.needs_input_grad[0]
or ctx.needs_input_grad[2]
or ctx.needs_input_grad[3]
or ctx.needs_input_grad[4]
or ctx.needs_input_grad[5]
or ctx.needs_input_grad[6]
or ctx.needs_input_grad[7]
)
and ctx.gamma < 1e-3
and not GAMMA_WARNING_EMITTED
):
warnings.warn(
"Optimizing for non-color parameters and having a gamma value < 1E-3! "
"This is probably not going to produce usable gradients."
)
GAMMA_WARNING_EMITTED = True
if ctx.mode == 0:
(
grad_pos,
grad_col,
grad_rad,
grad_cam_pos,
grad_pixel_0_0_center,
grad_pixel_vec_x,
grad_pixel_vec_y,
grad_opacity,
) = ctx.native_renderer.backward(
grad_im,
image,
info,
vert_pos,
vert_col,
vert_rad,
cam_pos,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
focal_length,
principal_point_offsets,
ctx.gamma,
ctx.max_depth,
ctx.min_depth,
bg_col,
opacity,
ctx.percent_allowed_difference,
ctx.max_n_hits,
ctx.mode,
ctx.needs_input_grad[0],
ctx.needs_input_grad[1],
ctx.needs_input_grad[2],
ctx.needs_input_grad[3]
or ctx.needs_input_grad[4]
or ctx.needs_input_grad[5]
or ctx.needs_input_grad[6],
ctx.needs_input_grad[13],
None, # No debug information provided.
)
else:
raise ValueError(
"Performing a backward pass for a "
"rendering with `mode != 0`! This is not possible."
)
return (
grad_pos,
grad_col,
grad_rad,
grad_cam_pos,
grad_pixel_0_0_center,
grad_pixel_vec_x,
grad_pixel_vec_y,
None, # focal_length
None, # principal_point_offsets
None, # gamma
None, # max_depth
None, # native_renderer
None, # min_depth
None, # bg_col
grad_opacity,
None, # percent_allowed_difference
None, # max_n_hits
None, # mode
None, # return_forward_info
)
class Renderer(torch.nn.Module):
"""
Differentiable rendering module for the Pulsar renderer.
Set the maximum number of balls to a reasonable value. It is used to determine
several buffer sizes. It is no problem to render less balls than this number,
but never more.
When optimizing for sphere positions, sphere radiuses or camera parameters you
have to use higher gamma values (closer to one) and larger sphere sizes: spheres
can only 'move' to areas that they cover, and only with higher gamma values exists
a gradient w.r.t. their color depending on their position.
Args:
* width: result image width in pixels.
* height: result image height in pixels.
* max_num_balls: the maximum number of balls this renderer will handle.
* orthogonal_projection: use an orthogonal instead of perspective projection.
Default: False.
* right_handed_system: use a right-handed instead of a left-handed coordinate
system. This is relevant for compatibility with other drawing or scanning
systems. Pulsar by default assumes a left-handed world and camera coordinate
system as known from mathematics with x-axis to the right, y axis up and z
axis for increasing depth along the optical axis. In the image coordinate
system, only the y axis is pointing down, leading still to a left-handed
system. If you set this to True, it is assuming a right-handed world and
camera coordinate system with x axis to the right, y axis to the top and
z axis decreasing along the optical axis. Again, the image coordinate
system has a flipped y axis, remaining a right-handed system.
Default: False.
* background_normalized_depth: the normalized depth the background is placed
at.
This is on a scale from 0. to 1. between the specified min and max depth
(see the forward function). The value 0. is the most furthest depth whereas
1. is the closest. Be careful when setting the background too far front - it
may hide elements in your scene. Default: EPS.
* n_channels: the number of image content channels to use. This is usually three
for regular color representations, but can be a higher or lower number.
Default: 3.
* n_track: the number of spheres to track for gradient calculation per pixel.
Only the closest n_track spheres will receive gradients. Default: 5.
"""
def __init__(
self,
width: int,
height: int,
max_num_balls: int,
orthogonal_projection: bool = False,
right_handed_system: bool = False,
background_normalized_depth: float = _C.EPS,
n_channels: int = 3,
n_track: int = 5,
):
super(Renderer, self).__init__()
# pyre-fixme[16]: Module `pytorch3d` has no attribute `_C`.
self._renderer = _C.PulsarRenderer(
width,
height,
max_num_balls,
orthogonal_projection,
right_handed_system,
background_normalized_depth,
n_channels,
n_track,
)
self.register_buffer("device_tracker", torch.zeros(1))
@staticmethod
def sphere_ids_from_result_info_nograd(result_info: torch.Tensor) -> torch.Tensor:
"""
Get the sphere IDs from a result info tensor.
"""
if result_info.ndim == 3:
return Renderer.sphere_ids_from_result_info_nograd(result_info[None, ...])
# pyre-fixme[16]: Module `pytorch3d` has no attribute `_C`.
return _C.pulsar_sphere_ids_from_result_info_nograd(result_info)
@staticmethod
def depth_map_from_result_info_nograd(result_info: torch.Tensor) -> torch.Tensor:
"""
Get the depth map from a result info tensor.
This returns a map of the same size as the image with just one channel
containing the closest intersection value at that position. Gradients
are not available for this tensor, but do note that you can use
`sphere_ids_from_result_info_nograd` to get the IDs of the spheres at
each position and directly create a loss on their depth if required.
The depth map contains -1. at positions where no intersection has
been detected.
"""
return result_info[..., 4]
@staticmethod
def _transform_cam_params(
cam_params: torch.Tensor,
width: int,
height: int,
orthogonal: bool,
right_handed: bool,
) -> Tuple[
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
torch.Tensor,
]:
"""
Transform 8 component camera parameter vector(s) to the internal camera
representation.
The input vectors consists of:
* 3 components for camera position,
* 3 components for camera rotation (three rotation angles) or
6 components as described in "On the Continuity of Rotation
Representations in Neural Networks" (Zhou et al.),
* focal length,
* the sensor width in world coordinates,
* [optional] the principal point offset in x and y.
The sensor height is inferred by pixel size and sensor width to obtain
quadratic pixels.
Args:
* cam_params: [Bx]{8, 10, 11, 13}, input tensors as described above.
* width: number of pixels in x direction.
* height: number of pixels in y direction.
* orthogonal: bool, whether an orthogonal projection is used
(does not use focal length).
* right_handed: bool, whether to use a right handed system
(negative z in camera direction).
Returns:
* pos_vec: the position vector in 3D,
* pixel_0_0_center: the center of the upper left pixel in world coordinates,
* pixel_vec_x: the step to move one pixel on the image x axis
in world coordinates,
* pixel_vec_y: the step to move one pixel on the image y axis
in world coordinates,
* focal_length: the focal lengths,
* principal_point_offsets: the principal point offsets in x, y.
"""
global AXANGLE_WARNING_EMITTED
# Set up all direction vectors, i.e., the sensor direction of all axes.
assert width > 0
assert height > 0
batch_processing = True
if cam_params.ndimension() == 1:
batch_processing = False
cam_params = cam_params[None, :]
batch_size = cam_params.size(0)
continuous_rep = True
if cam_params.shape[1] in [8, 10]:
if cam_params.requires_grad and not AXANGLE_WARNING_EMITTED:
warnings.warn(
"Using an axis angle representation for camera rotations. "
"This has discontinuities and should not be used for optimization. "
"Alternatively, use a six-component representation as described in "
"'On the Continuity of Rotation Representations in Neural Networks'"
" (Zhou et al.). "
"The `pytorch3d.transforms` module provides "
"facilities for using this representation."
)
AXANGLE_WARNING_EMITTED = True
continuous_rep = False
else:
assert cam_params.shape[1] in [11, 13]
pos_vec: torch.Tensor = cam_params[:, :3]
principal_point_offsets: torch.Tensor = torch.zeros(
(cam_params.shape[0], 2), dtype=torch.int32, device=cam_params.device
)
if continuous_rep:
rot_vec = cam_params[:, 3:9]
focal_length: torch.Tensor = cam_params[:, 9:10]
sensor_size_x = cam_params[:, 10:11]
if cam_params.shape[1] == 13:
principal_point_offsets: torch.Tensor = cam_params[:, 11:13].to(
torch.int32
)
else:
rot_vec = cam_params[:, 3:6]
focal_length: torch.Tensor = cam_params[:, 6:7]
sensor_size_x = cam_params[:, 7:8]
if cam_params.shape[1] == 10:
principal_point_offsets: torch.Tensor = cam_params[:, 8:10].to(
torch.int32
)
# Always get quadratic pixels.
pixel_size_x = sensor_size_x / float(width)
sensor_size_y = height * pixel_size_x
LOGGER.debug(
"Camera position: %s, rotation: %s. Focal length: %s.",
str(pos_vec),
str(rot_vec),
str(focal_length),
)
if continuous_rep:
rot_mat = rotation_6d_to_matrix(rot_vec)
else:
rot_mat = axis_angle_to_matrix(rot_vec)
sensor_dir_x = torch.matmul(
rot_mat,
torch.tensor(
[1.0, 0.0, 0.0], dtype=torch.float32, device=rot_mat.device
).repeat(batch_size, 1)[:, :, None],
)[:, :, 0]
sensor_dir_y = torch.matmul(
rot_mat,
torch.tensor(
[0.0, -1.0, 0.0], dtype=torch.float32, device=rot_mat.device
).repeat(batch_size, 1)[:, :, None],
)[:, :, 0]
sensor_dir_z = torch.matmul(
rot_mat,
torch.tensor(
[0.0, 0.0, 1.0], dtype=torch.float32, device=rot_mat.device
).repeat(batch_size, 1)[:, :, None],
)[:, :, 0]
if right_handed:
sensor_dir_z *= -1
LOGGER.debug(
"Sensor direction vectors: %s, %s, %s.",
str(sensor_dir_x),
str(sensor_dir_y),
str(sensor_dir_z),
)
if orthogonal:
sensor_center = pos_vec
else:
sensor_center = pos_vec + focal_length * sensor_dir_z
LOGGER.debug("Sensor center: %s.", str(sensor_center))
sensor_luc = ( # Sensor left upper corner.
sensor_center
- sensor_dir_x * (sensor_size_x / 2.0)
- sensor_dir_y * (sensor_size_y / 2.0)
)
LOGGER.debug("Sensor luc: %s.", str(sensor_luc))
pixel_size_x = sensor_size_x / float(width)
pixel_size_y = sensor_size_y / float(height)
LOGGER.debug(
"Pixel sizes (x): %s, (y) %s.", str(pixel_size_x), str(pixel_size_y)
)
pixel_vec_x: torch.Tensor = sensor_dir_x * pixel_size_x
pixel_vec_y: torch.Tensor = sensor_dir_y * pixel_size_y
pixel_0_0_center = sensor_luc + 0.5 * pixel_vec_x + 0.5 * pixel_vec_y
LOGGER.debug(
"Pixel 0 centers: %s, vec x: %s, vec y: %s.",
str(pixel_0_0_center),
str(pixel_vec_x),
str(pixel_vec_y),
)
if not orthogonal:
LOGGER.debug(
"Camera horizontal fovs: %s deg.",
str(
2.0
* torch.atan(0.5 * sensor_size_x / focal_length)
/ math.pi
* 180.0
),
)
LOGGER.debug(
"Camera vertical fovs: %s deg.",
str(
2.0
* torch.atan(0.5 * sensor_size_y / focal_length)
/ math.pi
* 180.0
),
)
# Reduce dimension.
focal_length: torch.Tensor = focal_length[:, 0]
if batch_processing:
return (
pos_vec,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
focal_length,
principal_point_offsets,
)
else:
return (
pos_vec[0],
pixel_0_0_center[0],
pixel_vec_x[0],
pixel_vec_y[0],
focal_length[0],
principal_point_offsets[0],
)
def forward(
self,
vert_pos: torch.Tensor,
vert_col: torch.Tensor,
vert_rad: torch.Tensor,
cam_params: torch.Tensor,
gamma: float,
max_depth: float,
min_depth: float = 0.0,
bg_col: Optional[torch.Tensor] = None,
opacity: Optional[torch.Tensor] = None,
percent_allowed_difference: float = 0.01,
max_n_hits: int = _C.MAX_UINT,
mode: int = 0,
return_forward_info: bool = False,
) -> Union[torch.Tensor, Tuple[torch.Tensor, Optional[torch.Tensor]]]:
"""
Rendering pass to create an image from the provided spheres and camera
parameters.
Args:
* vert_pos: vertex positions. [Bx]Nx3 tensor of positions in 3D space.
* vert_col: vertex colors. [Bx]NxK tensor of channels.
* vert_rad: vertex radii. [Bx]N tensor of radiuses, >0.
* cam_params: camera parameter(s). [Bx]8 tensor, consisting of:
- 3 components for camera position,
- 3 components for camera rotation (axis angle representation) or
6 components as described in "On the Continuity of Rotation
Representations in Neural Networks" (Zhou et al.),
- focal length,
- the sensor width in world coordinates,
- [optional] an offset for the principal point in x, y (no gradients).
* gamma: sphere transparency in [1.,1E-5], with 1 being mostly transparent.
[Bx]1.
* max_depth: maximum depth for spheres to render. Set this as tightly
as possible to have good numerical accuracy for gradients.
float > min_depth + eps.
* min_depth: a float with the minimum depth a sphere must have to be
rendered. Must be 0. or > max(focal_length) + eps.
* bg_col: K tensor with a background color to use or None (uses all ones).
* opacity: [Bx]N tensor of opacity values in [0., 1.] or None (uses all
ones).
* percent_allowed_difference: a float in [0., 1.[ with the maximum allowed
difference in color space. This is used to speed up the
computation. Default: 0.01.
* max_n_hits: a hard limit on the number of hits per ray. Default: max int.
* mode: render mode in {0, 1}. 0: render an image; 1: render the hit map.
* return_forward_info: whether to return a second map. This second map
contains 13 channels: first channel contains sm_m (the maximum
exponent factor observed), the second sm_d (the normalization
denominator, the sum of all coefficients), the third the maximum closest
possible intersection for a hit. The following channels alternate with
the float encoded integer index of a sphere and its weight. They are the
five spheres with the highest color contribution to this pixel color,
ordered descending. Default: False.
Returns:
* image: [Bx]HxWx3 float tensor with the resulting image.
* forw_info: [Bx]HxWx13 float forward information as described above, if
enabled.
"""
# The device tracker is registered as buffer.
# pyre-fixme[16]: `Renderer` has no attribute `device_tracker`.
self._renderer.device_tracker = self.device_tracker
(
pos_vec,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
focal_lengths,
principal_point_offsets,
) = Renderer._transform_cam_params(
cam_params,
self._renderer.width,
self._renderer.height,
self._renderer.orthogonal,
self._renderer.right_handed,
)
if (
focal_lengths.min().item() > 0.0
and max_depth > 10_000.0 * focal_lengths.min().item()
):
warnings.warn(
(
"Extreme ratio of `max_depth` vs. focal length detected "
"(%f vs. %f, ratio: %f). This will likely lead to "
"artifacts due to numerical instabilities."
)
% (
max_depth,
focal_lengths.min().item(),
max_depth / focal_lengths.min().item(),
)
)
# pyre-fixme[16]: `_Render` has no attribute `apply`.
ret_res = _Render.apply(
vert_pos,
vert_col,
vert_rad,
pos_vec,
pixel_0_0_center,
pixel_vec_x,
pixel_vec_y,
# Focal length and sensor size don't need gradients other than through
# `pixel_vec_x` and `pixel_vec_y`. The focal length is only used in the
# renderer to determine the projection areas of the balls.
focal_lengths,
# principal_point_offsets does not receive gradients.
principal_point_offsets,
gamma,
max_depth,
self._renderer,
min_depth,
bg_col,
opacity,
percent_allowed_difference,
max_n_hits,
mode,
(mode == 0) and return_forward_info,
)
if return_forward_info and mode != 0:
return ret_res, None
return ret_res
def extra_repr(self) -> str:
"""Extra information to print in pytorch graphs."""
return "width={}, height={}, max_num_balls={}".format(
self._renderer.width, self._renderer.height, self._renderer.max_num_balls
)