a formula for bin size for images over 64x64 (#90)

Summary:
Signed-off-by: Michele Sanna <sanna@arrival.com>

fixes the bin_size calculation with a formula for any image_size > 64. Matches the values chosen so far.

simple test:

```
import numpy as np
import matplotlib.pyplot as plt

image_size = np.arange(64, 2048)
bin_size = np.where(image_size <= 64, 8, (2 ** np.maximum(np.ceil(np.log2(image_size)) - 4, 4)).astype(int))

print(image_size)
print(bin_size)

for ims, bins in zip(image_size, bin_size):
    if ims <= 64:
        assert bins == 8
    elif ims <= 256:
        assert bins == 16
    elif ims <= 512:
        assert bins == 32
    elif ims <= 1024:
        assert bins == 64
    elif ims <= 2048:
        assert bins == 128

    assert (ims + bins - 1) // bins < 22

plt.plot(image_size, bin_size)
plt.grid()
plt.show()
```

![img](https://user-images.githubusercontent.com/54891577/75464693-795bcf00-597f-11ea-9061-26440211691c.png)
Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/90

Reviewed By: jcjohnson

Differential Revision: D21160372

Pulled By: nikhilaravi

fbshipit-source-id: 660cf5832f4ca5be243c435a6bed969596fc0188
This commit is contained in:
Michele Sanna 2020-04-24 14:53:59 -07:00 committed by Facebook GitHub Bot
parent c3d636dc8c
commit f8acecb6b3
6 changed files with 52 additions and 7 deletions

View File

@ -696,7 +696,7 @@ at::Tensor RasterizeMeshesCoarseCuda(
const int num_bins = 1 + (image_size - 1) / bin_size; // Divide round up.
const int M = max_faces_per_bin;
if (num_bins >= 22) {
if (num_bins >= kMaxFacesPerBin) {
std::stringstream ss;
ss << "Got " << num_bins << "; that's too many!";
AT_ERROR(ss.str());

View File

@ -17,6 +17,8 @@ __device__ inline float PixToNdc(int i, int S) {
// TODO: is 8 enough? Would increasing have performance considerations?
const int32_t kMaxPointsPerPixel = 150;
const int32_t kMaxFacesPerBin = 22;
template <typename T>
__device__ inline void BubbleSort(T* arr, int n) {
// Bubble sort. We only use it for tiny thread-local arrays (n < 8); in this

View File

@ -11,6 +11,10 @@ from pytorch3d import _C
# TODO make the epsilon user configurable
kEpsilon = 1e-8
# Maxinum number of faces per bins for
# coarse-to-fine rasterization
kMaxFacesPerBin = 22
def rasterize_meshes(
meshes,
@ -107,12 +111,23 @@ def rasterize_meshes(
# TODO better heuristics for bin size.
if image_size <= 64:
bin_size = 8
elif image_size <= 256:
bin_size = 16
elif image_size <= 512:
bin_size = 32
elif image_size <= 1024:
bin_size = 64
else:
# Heuristic based formula maps image_size -> bin_size as follows:
# image_size < 64 -> 8
# 16 < image_size < 256 -> 16
# 256 < image_size < 512 -> 32
# 512 < image_size < 1024 -> 64
# 1024 < image_size < 2048 -> 128
bin_size = int(2 ** max(np.ceil(np.log2(image_size)) - 4, 4))
if bin_size != 0:
# There is a limit on the number of faces per bin in the cuda kernel.
faces_per_bin = 1 + (image_size - 1) // bin_size
if faces_per_bin >= kMaxFacesPerBin:
raise ValueError(
"bin_size too small, number of faces per bin must be less than %d; got %d"
% (kMaxFacesPerBin, faces_per_bin)
)
if max_faces_per_bin is None:
max_faces_per_bin = int(max(10000, verts_packed.shape[0] / 5))

View File

@ -7,6 +7,11 @@ from pytorch3d import _C
from pytorch3d.renderer.mesh.rasterize_meshes import pix_to_ndc
# Maxinum number of faces per bins for
# coarse-to-fine rasterization
kMaxPointsPerBin = 22
# TODO(jcjohns): Support non-square images
def rasterize_points(
pointclouds,
@ -82,6 +87,15 @@ def rasterize_points(
elif image_size <= 1024:
bin_size = 64
if bin_size != 0:
# There is a limit on the number of points per bin in the cuda kernel.
points_per_bin = 1 + (image_size - 1) // bin_size
if points_per_bin >= kMaxPointsPerBin:
raise ValueError(
"bin_size too small, number of points per bin must be less than %d; got %d"
% (kMaxPointsPerBin, points_per_bin)
)
if max_points_per_bin is None:
max_points_per_bin = int(max(10000, points_packed.shape[0] / 5))

View File

@ -382,6 +382,13 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
args = ()
self._compare_impls(fn1, fn2, args, args, verts1, verts2, compare_grads=True)
def test_bin_size_error(self):
meshes = ico_sphere(2)
image_size = 1024
bin_size = 16
with self.assertRaisesRegex(ValueError, "bin_size too small"):
rasterize_meshes(meshes, image_size, 0.0, 2, bin_size)
def _test_back_face_culling(self, rasterize_meshes_fn, device, bin_size):
# Square based pyramid mesh.
# fmt: off

View File

@ -212,6 +212,13 @@ class TestRasterizePoints(TestCaseMixin, unittest.TestCase):
if compare_grads:
self.assertClose(grad_points1, grad_points2, atol=2e-6)
def test_bin_size_error(self):
points = Pointclouds(points=torch.rand(5, 100, 3))
image_size = 1024
bin_size = 16
with self.assertRaisesRegex(ValueError, "bin_size too small"):
rasterize_points(points, image_size, 0.0, 2, bin_size=bin_size)
def _test_behind_camera(self, rasterize_points_fn, device, bin_size=None):
# Test case where all points are behind the camera -- nothing should
# get rasterized