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fix recent lint

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Summary: Flowing of compositing comments

Reviewed By: nikhilaravi

Differential Revision: D20556707

fbshipit-source-id: 4abdc85e4f65abd41c4a890b6895bc5e95b4576b
This commit is contained in:
Jeremy Reizenstein 2020-03-30 06:16:09 -07:00 committed by Facebook GitHub Bot
parent d57daa6f85
commit 27eb791e2f
4 changed files with 35 additions and 34 deletions
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6
dev/linter.sh
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@ -28,11 +28,11 @@ echo "Running clang-format ..."
clangformat=$(command -v clang-format-8 || echo clang-format)
find "${DIR}" -regex ".*\.\(cpp\|c\|cc\|cu\|cuh\|cxx\|h\|hh\|hpp\|hxx\|tcc\|mm\|m\)" -print0 | xargs -0 "${clangformat}" -i
# (cd "${DIR}"; command -v arc > /dev/null && arc lint) || true
# Run pyre internally only.
# Run arc and pyre internally only.
if [[ -f tests/TARGETS ]]
then
(cd "${DIR}"; command -v arc > /dev/null && arc lint) || true
echo "Running pyre..."
echo "To restart/kill pyre server, run 'pyre restart' or 'pyre kill' in fbcode/"
( cd ~/fbsource/fbcode; pyre -l vision/fair/pytorch3d/ )

57
pytorch3d/renderer/compositing.py
View File

@ -21,7 +21,7 @@ class CompositeParams(NamedTuple):
class _CompositeAlphaPoints(torch.autograd.Function):
"""
Composite features within a z-buffer using alpha compositing. Given a zbuffer
Composite features within a z-buffer using alpha compositing. Given a z-buffer
with corresponding features and weights, these values are accumulated according
to their weights such that features nearer in depth contribute more to the final
feature than ones further away.
@ -37,9 +37,9 @@ class _CompositeAlphaPoints(torch.autograd.Function):
Values should be in the interval [0, 1].
pointsidx: int32 Tensor of shape (N, points_per_pixel, image_size, image_size)
giving the indices of the nearest points at each pixel, sorted in z-order.
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the feature of
the kth closest point (along the z-direction) to pixel (y, x) in batch element n.
This is weighted by alphas[n, k, y, x].
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the
feature of the kth closest point (along the z-direction) to pixel (y, x) in
batch element n. This is weighted by alphas[n, k, y, x].
Returns:
weighted_fs: Tensor of shape (N, C, image_size, image_size)
@ -69,7 +69,7 @@ class _CompositeAlphaPoints(torch.autograd.Function):
def alpha_composite(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tensor:
"""
Composite features within a z-buffer using alpha compositing. Given a zbuffer
Composite features within a z-buffer using alpha compositing. Given a z-buffer
with corresponding features and weights, these values are accumulated according
to their weights such that features nearer in depth contribute more to the final
feature than ones further away.
@ -80,15 +80,16 @@ def alpha_composite(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tens
Args:
pt_clds: Tensor of shape (N, C, P) giving the features of each point (can use RGB for example).
pt_clds: Tensor of shape (N, C, P) giving the features of each point (can use
RGB for example).
alphas: float32 Tensor of shape (N, points_per_pixel, image_size,
image_size) giving the weight of each point in the z-buffer.
Values should be in the interval [0, 1].
pointsidx: int32 Tensor of shape (N, points_per_pixel, image_size, image_size)
giving the indices of the nearest points at each pixel, sorted in z-order.
Concretely pointsidx[n, k, y, x] = p means that features[n, :, p] is the feature of
the kth closest point (along the z-direction) to pixel (y, x) in batch element n.
This is weighted by alphas[n, k, y, x].
Concretely pointsidx[n, k, y, x] = p means that features[n, :, p] is the
feature of the kth closest point (along the z-direction) to pixel (y, x) in
batch element n. This is weighted by alphas[n, k, y, x].
Returns:
Combined features: Tensor of shape (N, C, image_size, image_size)
@ -99,10 +100,10 @@ def alpha_composite(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tens
class _CompositeNormWeightedSumPoints(torch.autograd.Function):
"""
Composite features within a z-buffer using normalized weighted sum. Given a zbuffer
Composite features within a z-buffer using normalized weighted sum. Given a z-buffer
with corresponding features and weights, these values are accumulated
according to their weights such that depth is ignored; the weights are used to perform
a weighted sum.
according to their weights such that depth is ignored; the weights are used to
perform a weighted sum.
Concretely this means:
weighted_fs[b,c,i,j] =
@ -115,9 +116,9 @@ class _CompositeNormWeightedSumPoints(torch.autograd.Function):
Values should be in the interval [0, 1].
pointsidx: int32 Tensor of shape (N, points_per_pixel, image_size, image_size)
giving the indices of the nearest points at each pixel, sorted in z-order.
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the feature of
the kth closest point (along the z-direction) to pixel (y, x) in batch element n.
This is weighted by alphas[n, k, y, x].
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the
feature of the kth closest point (along the z-direction) to pixel (y, x) in
batch element n. This is weighted by alphas[n, k, y, x].
Returns:
weighted_fs: Tensor of shape (N, C, image_size, image_size)
@ -147,10 +148,10 @@ class _CompositeNormWeightedSumPoints(torch.autograd.Function):
def norm_weighted_sum(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tensor:
"""
Composite features within a z-buffer using normalized weighted sum. Given a zbuffer
Composite features within a z-buffer using normalized weighted sum. Given a z-buffer
with corresponding features and weights, these values are accumulated
according to their weights such that depth is ignored; the weights are used to perform
a weighted sum.
according to their weights such that depth is ignored; the weights are used to
perform a weighted sum.
Concretely this means:
weighted_fs[b,c,i,j] =
@ -164,9 +165,9 @@ def norm_weighted_sum(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Te
Values should be in the interval [0, 1].
pointsidx: int32 Tensor of shape (N, points_per_pixel, image_size, image_size)
giving the indices of the nearest points at each pixel, sorted in z-order.
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the feature of
the kth closest point (along the z-direction) to pixel (y, x) in batch element n.
This is weighted by alphas[n, k, y, x].
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the
feature of the kth closest point (along the z-direction) to pixel (y, x) in
batch element n. This is weighted by alphas[n, k, y, x].
Returns:
Combined features: Tensor of shape (N, C, image_size, image_size)
@ -177,7 +178,7 @@ def norm_weighted_sum(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Te
class _CompositeWeightedSumPoints(torch.autograd.Function):
"""
Composite features within a z-buffer using normalized weighted sum. Given a zbuffer
Composite features within a z-buffer using normalized weighted sum. Given a z-buffer
with corresponding features and weights, these values are accumulated
according to their weights such that depth is ignored; the weights are used to
perform a weighted sum. As opposed to norm weighted sum, the weights are not
@ -193,9 +194,9 @@ class _CompositeWeightedSumPoints(torch.autograd.Function):
Values should be in the interval [0, 1].
pointsidx: int32 Tensor of shape (N, points_per_pixel, image_size, image_size)
giving the indices of the nearest points at each pixel, sorted in z-order.
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the feature of
the kth closest point (along the z-direction) to pixel (y, x) in batch element n.
This is weighted by alphas[n, k, y, x].
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the
feature of the kth closest point (along the z-direction) to pixel (y, x) in
batch element n. This is weighted by alphas[n, k, y, x].
Returns:
weighted_fs: Tensor of shape (N, C, image_size, image_size)
@ -235,9 +236,9 @@ def weighted_sum(pointsidx, alphas, pt_clds, blend_params=None) -> torch.Tensor:
Values should be in the interval [0, 1].
pointsidx: int32 Tensor of shape (N, points_per_pixel, image_size, image_size)
giving the indices of the nearest points at each pixel, sorted in z-order.
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the feature of
the kth closest point (along the z-direction) to pixel (y, x) in batch element n.
This is weighted by alphas[n, k, y, x].
Concretely pointsidx[n, k, y, x] = p means that features[:, p] is the
feature of the kth closest point (along the z-direction) to pixel (y, x) in
batch element n. This is weighted by alphas[n, k, y, x].
Returns:
Combined features: Tensor of shape (N, C, image_size, image_size)

3
pytorch3d/structures/pointclouds.py
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@ -245,7 +245,8 @@ class Pointclouds(object):
Returns:
3-element tuple of list, padded, num_channels.
If aux_input is list, then padded is None. If aux_input is a tensor, then list is None.
If aux_input is list, then padded is None. If aux_input is a tensor,
then list is None.
"""
if aux_input is None or self._N == 0:
return None, None, None

1
tests/test_nearest_neighbor_points.py
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@ -1,7 +1,6 @@
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import unittest
from itertools import product
import torch
from pytorch3d import _C