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Bug fix for case where aspect ratio is a float

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Summary:
- Fix the calculation of the non square NDC range when the H and W are not integer multiples.
- Add test for this case

Reviewed By: gkioxari

Differential Revision: D26613213

fbshipit-source-id: df6763cac602e9f1d516b41b432c4d2cfbaa356d
This commit is contained in:
Nikhila Ravi 2021-02-24 10:05:07 -08:00 committed by Facebook GitHub Bot
parent 0345f860d4
commit 13429640d3
4 changed files with 15 additions and 18 deletions
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4
pytorch3d/csrc/rasterize_points/rasterization_utils.cuh
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@ -10,7 +10,9 @@
__device__ inline float NonSquareNdcRange(int S1, int S2) {
float range = 2.0f;
if (S1 > S2) {
range = ((S1 / S2) * range);
// First multiply S1 by float range so that division results
// in a float value.
range = (S1 * range) / S2;
}
return range;
}

2
pytorch3d/csrc/rasterize_points/rasterization_utils.h
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@ -10,7 +10,7 @@
inline float NonSquareNdcRange(int S1, int S2) {
float range = 2.0f;
if (S1 > S2) {
range = ((S1 / S2) * range);
range = (S1 * range) / S2;
}
return range;
}

11
pytorch3d/renderer/points/rasterize_points.py
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@ -2,6 +2,7 @@
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
# pyre-fixme[21]: Could not find name `_C` in `pytorch3d`.
@ -120,15 +121,7 @@ def rasterize_points(
# Binned CPU rasterization not fully implemented
bin_size = 0
else:
# TODO: These heuristics are not well-thought out!
if max_image_size <= 64:
bin_size = 8
elif max_image_size <= 256:
bin_size = 16
elif max_image_size <= 512:
bin_size = 32
elif max_image_size <= 1024:
bin_size = 64
bin_size = int(2 ** max(np.ceil(np.log2(max_image_size)) - 4, 4))
if bin_size != 0:
# There is a limit on the number of points per bin in the cuda kernel.

16
tests/test_rasterize_rectangle_images.py
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@ -314,7 +314,7 @@ class TestRasterizeRectangleImagesMeshes(TestCaseMixin, unittest.TestCase):
# Finally check the gradients of the input vertices for
# the square and non square case
self.assertClose(verts_square.grad, grad_tensor.grad, rtol=2e-4)
self.assertClose(verts_square.grad, grad_tensor.grad, rtol=3e-4)
def test_gpu(self):
"""
@ -323,8 +323,9 @@ class TestRasterizeRectangleImagesMeshes(TestCaseMixin, unittest.TestCase):
dists, zbuf, bary are all the same for the square
region which is present in both images.
"""
# Test both cases: (W > H), (H > W)
image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128)]
# Test both cases: (W > H), (H > W) as well as the case where
# H and W are not integer multiples of each other (i.e. float aspect ratio)
image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128), (600, 1110)]
devices = ["cuda:0"]
blurs = [0.0, 0.001]
@ -391,7 +392,7 @@ class TestRasterizeRectangleImagesMeshes(TestCaseMixin, unittest.TestCase):
"""
# Test both when (W > H) and (H > W).
# Using smaller image sizes here as the Python rasterizer is really slow.
image_sizes = [(32, 64), (64, 32)]
image_sizes = [(32, 64), (64, 32), (60, 110)]
devices = ["cpu"]
blurs = [0.0, 0.001]
batch_sizes = [1]
@ -646,8 +647,9 @@ class TestRasterizeRectangleImagesPointclouds(TestCaseMixin, unittest.TestCase):
dists, zbuf, idx are all the same for the square
region which is present in both images.
"""
# Test both cases: (W > H), (H > W)
image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128)]
# Test both cases: (W > H), (H > W) as well as the case where
# H and W are not integer multiples of each other (i.e. float aspect ratio)
image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128), (600, 1110)]
devices = ["cuda:0"]
blurs = [5e-2]
@ -713,7 +715,7 @@ class TestRasterizeRectangleImagesPointclouds(TestCaseMixin, unittest.TestCase):
"""
# Test both when (W > H) and (H > W).
# Using smaller image sizes here as the Python rasterizer is really slow.
image_sizes = [(32, 64), (64, 32)]
image_sizes = [(32, 64), (64, 32), (60, 110)]
devices = ["cpu"]
blurs = [5e-2]
batch_sizes = [1]