mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
3d011a9198
Summary:
## Context
Bins are used in mipnerf to allow to manipulate easily intervals. For example, by doing the following, `bins[..., :-1]` you will obtain all the left coordinates of your intervals, while doing `bins[..., 1:]` is equals to the right coordinates of your intervals.
We introduce here the support of bins like in MipNerf implementation.
## RayPointRefiner
Small changes have been made to modify RayPointRefiner.
- If bins is None
```
mids = torch.lerp(ray_bundle.lengths[..., 1:], ray_bundle.lengths[…, :-1], 0.5)
z_samples = sample_pdf(
mids, # [..., npt]
weights[..., 1:-1], # [..., npt - 1]
….
)
```
- If bins is not None
In the MipNerf implementation the sampling is done on all the bins. It allows us to use the full weights tensor without slashing it.
```
z_samples = sample_pdf(
ray_bundle.bins, # [..., npt + 1]
weights, # [..., npt]
...
)
```
## RayMarcher
Add a ray_deltas optional argument. If None, keep the same deltas computation from ray_lengths.
Reviewed By: shapovalov
Differential Revision: D46389092
fbshipit-source-id: d4f1963310065bd31c1c7fac1adfe11cbeaba606
158 lines
5.8 KiB
Python
158 lines
5.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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import unittest
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from itertools import product
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import torch
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from pytorch3d.implicitron.models.renderer.ray_point_refiner import (
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apply_blurpool_on_weights,
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RayPointRefiner,
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)
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from pytorch3d.implicitron.models.renderer.ray_sampler import ImplicitronRayBundle
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from tests.common_testing import TestCaseMixin
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class TestRayPointRefiner(TestCaseMixin, unittest.TestCase):
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def test_simple(self):
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length = 15
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n_pts_per_ray = 10
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for add_input_samples, use_blurpool in product([False, True], [False, True]):
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ray_point_refiner = RayPointRefiner(
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n_pts_per_ray=n_pts_per_ray,
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random_sampling=False,
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add_input_samples=add_input_samples,
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blurpool_weights=use_blurpool,
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)
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lengths = torch.arange(length, dtype=torch.float32).expand(3, 25, length)
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bundle = ImplicitronRayBundle(
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lengths=lengths,
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origins=None,
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directions=None,
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xys=None,
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camera_ids=None,
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camera_counts=None,
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)
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weights = torch.ones(3, 25, length)
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refined = ray_point_refiner(bundle, weights)
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self.assertIsNone(refined.directions)
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self.assertIsNone(refined.origins)
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self.assertIsNone(refined.xys)
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expected = torch.linspace(0.5, length - 1.5, n_pts_per_ray)
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expected = expected.expand(3, 25, n_pts_per_ray)
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if add_input_samples:
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full_expected = torch.cat((lengths, expected), dim=-1).sort()[0]
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else:
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full_expected = expected
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self.assertClose(refined.lengths, full_expected)
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ray_point_refiner_random = RayPointRefiner(
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n_pts_per_ray=n_pts_per_ray,
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random_sampling=True,
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add_input_samples=add_input_samples,
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blurpool_weights=use_blurpool,
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)
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refined_random = ray_point_refiner_random(bundle, weights)
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lengths_random = refined_random.lengths
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self.assertEqual(lengths_random.shape, full_expected.shape)
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if not add_input_samples:
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self.assertGreater(lengths_random.min().item(), 0.5)
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self.assertLess(lengths_random.max().item(), length - 1.5)
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# Check sorted
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self.assertTrue(
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(lengths_random[..., 1:] - lengths_random[..., :-1] > 0).all()
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)
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def test_simple_use_bins(self):
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"""
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Same spirit than test_simple but use bins in the ImplicitronRayBunle.
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It has been duplicated to avoid cognitive overload while reading the
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test (lot of if else).
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"""
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length = 15
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n_pts_per_ray = 10
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for add_input_samples, use_blurpool in product([False, True], [False, True]):
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ray_point_refiner = RayPointRefiner(
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n_pts_per_ray=n_pts_per_ray,
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random_sampling=False,
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add_input_samples=add_input_samples,
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)
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bundle = ImplicitronRayBundle(
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lengths=None,
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bins=torch.arange(length + 1, dtype=torch.float32).expand(
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3, 25, length + 1
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),
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origins=None,
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directions=None,
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xys=None,
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camera_ids=None,
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camera_counts=None,
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)
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weights = torch.ones(3, 25, length)
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refined = ray_point_refiner(bundle, weights, blurpool_weights=use_blurpool)
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self.assertIsNone(refined.directions)
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self.assertIsNone(refined.origins)
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self.assertIsNone(refined.xys)
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expected_bins = torch.linspace(0, length, n_pts_per_ray + 1)
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expected_bins = expected_bins.expand(3, 25, n_pts_per_ray + 1)
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if add_input_samples:
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expected_bins = torch.cat((bundle.bins, expected_bins), dim=-1).sort()[
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0
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]
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full_expected = torch.lerp(
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expected_bins[..., :-1], expected_bins[..., 1:], 0.5
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)
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self.assertClose(refined.lengths, full_expected)
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ray_point_refiner_random = RayPointRefiner(
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n_pts_per_ray=n_pts_per_ray,
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random_sampling=True,
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add_input_samples=add_input_samples,
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)
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refined_random = ray_point_refiner_random(
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bundle, weights, blurpool_weights=use_blurpool
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)
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lengths_random = refined_random.lengths
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self.assertEqual(lengths_random.shape, full_expected.shape)
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if not add_input_samples:
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self.assertGreater(lengths_random.min().item(), 0)
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self.assertLess(lengths_random.max().item(), length)
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# Check sorted
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self.assertTrue(
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(lengths_random[..., 1:] - lengths_random[..., :-1] > 0).all()
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)
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def test_apply_blurpool_on_weights(self):
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weights = torch.tensor(
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[
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[0.5, 0.6, 0.7],
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[0.5, 0.3, 0.9],
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]
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)
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expected_weights = 0.5 * torch.tensor(
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[
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[0.5 + 0.6, 0.6 + 0.7, 0.7 + 0.7],
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[0.5 + 0.5, 0.5 + 0.9, 0.9 + 0.9],
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]
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)
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out_weights = apply_blurpool_on_weights(weights)
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self.assertTrue(torch.allclose(out_weights, expected_weights))
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def test_shapes_apply_blurpool_on_weights(self):
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weights = torch.randn((5, 4, 3, 2, 1))
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out_weights = apply_blurpool_on_weights(weights)
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self.assertEqual((5, 4, 3, 2, 1), out_weights.shape)
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