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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/ )

59
pytorch3d/renderer/compositing.py
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@ -21,11 +21,11 @@ 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.
Concretely this means:
weighted_fs[b,c,i,j] = sum_k cum_alpha_k * features[c,pointsidx[b,k,i,j]]
cum_alpha_k = alphas[b,k,i,j] * prod_l=0..k-1 (1 - alphas[b,l,i,j])
@ -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