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Bug fix for case where aspect ratio is a float
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
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@ -10,7 +10,9 @@
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__device__ inline float NonSquareNdcRange(int S1, int S2) {
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float range = 2.0f;
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if (S1 > S2) {
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range = ((S1 / S2) * range);
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// First multiply S1 by float range so that division results
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// in a float value.
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range = (S1 * range) / S2;
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}
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return range;
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}
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@ -10,7 +10,7 @@
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inline float NonSquareNdcRange(int S1, int S2) {
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float range = 2.0f;
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if (S1 > S2) {
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range = ((S1 / S2) * range);
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range = (S1 * range) / S2;
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}
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return range;
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}
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@ -2,6 +2,7 @@
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import torch
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# pyre-fixme[21]: Could not find name `_C` in `pytorch3d`.
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@ -120,15 +121,7 @@ def rasterize_points(
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# Binned CPU rasterization not fully implemented
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bin_size = 0
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else:
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# TODO: These heuristics are not well-thought out!
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if max_image_size <= 64:
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bin_size = 8
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elif max_image_size <= 256:
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bin_size = 16
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elif max_image_size <= 512:
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bin_size = 32
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elif max_image_size <= 1024:
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bin_size = 64
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bin_size = int(2 ** max(np.ceil(np.log2(max_image_size)) - 4, 4))
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if bin_size != 0:
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# There is a limit on the number of points per bin in the cuda kernel.
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@ -314,7 +314,7 @@ class TestRasterizeRectangleImagesMeshes(TestCaseMixin, unittest.TestCase):
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# Finally check the gradients of the input vertices for
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# the square and non square case
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self.assertClose(verts_square.grad, grad_tensor.grad, rtol=2e-4)
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self.assertClose(verts_square.grad, grad_tensor.grad, rtol=3e-4)
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def test_gpu(self):
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"""
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@ -323,8 +323,9 @@ class TestRasterizeRectangleImagesMeshes(TestCaseMixin, unittest.TestCase):
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dists, zbuf, bary are all the same for the square
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region which is present in both images.
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"""
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# Test both cases: (W > H), (H > W)
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image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128)]
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# Test both cases: (W > H), (H > W) as well as the case where
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# H and W are not integer multiples of each other (i.e. float aspect ratio)
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image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128), (600, 1110)]
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devices = ["cuda:0"]
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blurs = [0.0, 0.001]
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@ -391,7 +392,7 @@ class TestRasterizeRectangleImagesMeshes(TestCaseMixin, unittest.TestCase):
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"""
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# Test both when (W > H) and (H > W).
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# Using smaller image sizes here as the Python rasterizer is really slow.
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image_sizes = [(32, 64), (64, 32)]
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image_sizes = [(32, 64), (64, 32), (60, 110)]
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devices = ["cpu"]
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blurs = [0.0, 0.001]
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batch_sizes = [1]
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@ -646,8 +647,9 @@ class TestRasterizeRectangleImagesPointclouds(TestCaseMixin, unittest.TestCase):
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dists, zbuf, idx are all the same for the square
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region which is present in both images.
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"""
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# Test both cases: (W > H), (H > W)
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image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128)]
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# Test both cases: (W > H), (H > W) as well as the case where
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# H and W are not integer multiples of each other (i.e. float aspect ratio)
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image_sizes = [(64, 128), (128, 64), (128, 256), (256, 128), (600, 1110)]
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devices = ["cuda:0"]
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blurs = [5e-2]
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@ -713,7 +715,7 @@ class TestRasterizeRectangleImagesPointclouds(TestCaseMixin, unittest.TestCase):
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"""
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# Test both when (W > H) and (H > W).
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# Using smaller image sizes here as the Python rasterizer is really slow.
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image_sizes = [(32, 64), (64, 32)]
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image_sizes = [(32, 64), (64, 32), (60, 110)]
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devices = ["cpu"]
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blurs = [5e-2]
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batch_sizes = [1]
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