mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
vert_align for Pointclouds object
Reviewed By: gkioxari Differential Revision: D21088730 fbshipit-source-id: f8c125ac8c8009d45712ae63237ca64acf1faf45
This commit is contained in:
parent
e19df58766
commit
f25af96959
@ -25,10 +25,10 @@ def vert_align(
|
||||
feats: FloatTensor of shape (N, C, H, W) representing image features
|
||||
from which to sample or a list of features each with potentially
|
||||
different C, H or W dimensions.
|
||||
verts: FloatTensor of shape (N, V, 3) or an object (e.g. Meshes) with
|
||||
'verts_padded' as an attribute giving the (x, y, z) vertex positions
|
||||
for which to sample. (x, y) verts should be normalized such that
|
||||
(-1, -1) corresponds to top-left and (+1, +1) to bottom-right
|
||||
verts: FloatTensor of shape (N, V, 3) or an object (e.g. Meshes or Pointclouds)
|
||||
with `verts_padded' or `points_padded' as an attribute giving the (x, y, z)
|
||||
vertex positions for which to sample. (x, y) verts should be normalized such
|
||||
that (-1, -1) corresponds to top-left and (+1, +1) to bottom-right
|
||||
location in the input feature map.
|
||||
return_packed: (bool) Indicates whether to return packed features
|
||||
interp_mode: (str) Specifies how to interpolate features.
|
||||
@ -44,13 +44,11 @@ def vert_align(
|
||||
resolution agnostic. Default: ``True``
|
||||
|
||||
Returns:
|
||||
feats_sampled: FloatTensor of shape (N, V, C) giving sampled features for
|
||||
each vertex. If feats is a list, we return concatentated
|
||||
features in axis=2 of shape (N, V, sum(C_n)) where
|
||||
C_n = feats[n].shape[1]. If return_packed = True, the
|
||||
features are transformed to a packed representation
|
||||
of shape (sum(V), C)
|
||||
|
||||
feats_sampled: FloatTensor of shape (N, V, C) giving sampled features for each
|
||||
vertex. If feats is a list, we return concatentated features in axis=2 of
|
||||
shape (N, V, sum(C_n)) where C_n = feats[n].shape[1].
|
||||
If return_packed = True, the features are transformed to a packed
|
||||
representation of shape (sum(V), C)
|
||||
"""
|
||||
if torch.is_tensor(verts):
|
||||
if verts.dim() != 3:
|
||||
@ -58,8 +56,13 @@ def vert_align(
|
||||
grid = verts
|
||||
elif hasattr(verts, "verts_padded"):
|
||||
grid = verts.verts_padded()
|
||||
elif hasattr(verts, "points_padded"):
|
||||
grid = verts.points_padded()
|
||||
else:
|
||||
raise ValueError("verts must be a tensor or have a `verts_padded` attribute")
|
||||
raise ValueError(
|
||||
"verts must be a tensor or have a "
|
||||
+ "`points_padded' or`verts_padded` attribute."
|
||||
)
|
||||
|
||||
grid = grid[:, None, :, :2] # (N, 1, V, 2)
|
||||
|
||||
|
@ -8,12 +8,13 @@ import torch.nn.functional as F
|
||||
from common_testing import TestCaseMixin
|
||||
from pytorch3d.ops.vert_align import vert_align
|
||||
from pytorch3d.structures.meshes import Meshes
|
||||
from pytorch3d.structures.pointclouds import Pointclouds
|
||||
|
||||
|
||||
class TestVertAlign(TestCaseMixin, unittest.TestCase):
|
||||
@staticmethod
|
||||
def vert_align_naive(
|
||||
feats, verts_or_meshes, return_packed: bool = False, align_corners: bool = True
|
||||
feats, verts, return_packed: bool = False, align_corners: bool = True
|
||||
):
|
||||
"""
|
||||
Naive implementation of vert_align.
|
||||
@ -28,12 +29,12 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
|
||||
out_i_feats = []
|
||||
for feat in feats:
|
||||
feats_i = feat[i][None, :, :, :] # (1, C, H, W)
|
||||
if torch.is_tensor(verts_or_meshes):
|
||||
grid = verts_or_meshes[i][None, None, :, :2] # (1, 1, V, 2)
|
||||
elif hasattr(verts_or_meshes, "verts_list"):
|
||||
grid = verts_or_meshes.verts_list()[i][
|
||||
None, None, :, :2
|
||||
] # (1, 1, V, 2)
|
||||
if torch.is_tensor(verts):
|
||||
grid = verts[i][None, None, :, :2] # (1, 1, V, 2)
|
||||
elif hasattr(verts, "verts_list"):
|
||||
grid = verts.verts_list()[i][None, None, :, :2] # (1, 1, V, 2)
|
||||
elif hasattr(verts, "points_list"):
|
||||
grid = verts.points_list()[i][None, None, :, :2] # (1, 1, V, 2)
|
||||
else:
|
||||
raise ValueError("verts_or_meshes is invalid")
|
||||
feat_sampled_i = F.grid_sample(
|
||||
@ -56,7 +57,9 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
|
||||
return out_feats
|
||||
|
||||
@staticmethod
|
||||
def init_meshes(num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000):
|
||||
def init_meshes(
|
||||
num_meshes: int = 10, num_verts: int = 1000, num_faces: int = 3000
|
||||
) -> Meshes:
|
||||
device = torch.device("cuda:0")
|
||||
verts_list = []
|
||||
faces_list = []
|
||||
@ -74,6 +77,20 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
|
||||
|
||||
return meshes
|
||||
|
||||
@staticmethod
|
||||
def init_pointclouds(num_clouds: int = 10, num_points: int = 1000) -> Pointclouds:
|
||||
device = torch.device("cuda:0")
|
||||
points_list = []
|
||||
for _ in range(num_clouds):
|
||||
points = (
|
||||
torch.rand((num_points, 3), dtype=torch.float32, device=device) * 2.0
|
||||
- 1.0
|
||||
) # points in the space of [-1, 1]
|
||||
points_list.append(points)
|
||||
pointclouds = Pointclouds(points=points_list)
|
||||
|
||||
return pointclouds
|
||||
|
||||
@staticmethod
|
||||
def init_feats(batch_size: int = 10, num_channels: int = 256, device: str = "cuda"):
|
||||
H, W = [14, 28], [14, 28]
|
||||
@ -99,6 +116,27 @@ class TestVertAlign(TestCaseMixin, unittest.TestCase):
|
||||
naive_out = TestVertAlign.vert_align_naive(feats[0], meshes, return_packed=True)
|
||||
self.assertClose(out, naive_out)
|
||||
|
||||
def test_vert_align_with_pointclouds(self):
|
||||
"""
|
||||
Test vert align vs naive implementation with meshes.
|
||||
"""
|
||||
pointclouds = TestVertAlign.init_pointclouds(10, 1000)
|
||||
feats = TestVertAlign.init_feats(10, 256)
|
||||
|
||||
# feats in list
|
||||
out = vert_align(feats, pointclouds, return_packed=True)
|
||||
naive_out = TestVertAlign.vert_align_naive(
|
||||
feats, pointclouds, return_packed=True
|
||||
)
|
||||
self.assertClose(out, naive_out)
|
||||
|
||||
# feats as tensor
|
||||
out = vert_align(feats[0], pointclouds, return_packed=True)
|
||||
naive_out = TestVertAlign.vert_align_naive(
|
||||
feats[0], pointclouds, return_packed=True
|
||||
)
|
||||
self.assertClose(out, naive_out)
|
||||
|
||||
def test_vert_align_with_verts(self):
|
||||
"""
|
||||
Test vert align vs naive implementation with verts as tensor.
|
||||
|
Loading…
x
Reference in New Issue
Block a user