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defaulted grid_sizes in points2vols
Summary: Fix #873, that grid_sizes defaults to the wrong dtype in points2volumes code, and mask doesn't have a proper default. Reviewed By: nikhilaravi Differential Revision: D31503545 fbshipit-source-id: fa32a1a6074fc7ac7bdb362edfb5e5839866a472
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@ -5,7 +5,7 @@
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# LICENSE file in the root directory of this source tree.
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import math
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from typing import Tuple, Optional
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from typing import Optional, Tuple
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import torch
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import torch.nn.functional as F
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@ -364,7 +364,7 @@ def add_points_features_to_volume_densities_features(
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# grid sizes shape (minibatch, 3)
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grid_sizes = (
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torch.LongTensor(list(volume_densities.shape[2:]))
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.to(volume_densities)
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.to(volume_densities.device)
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.expand(volume_densities.shape[0], 3)
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)
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@ -386,6 +386,10 @@ def add_points_features_to_volume_densities_features(
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splat = False
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else:
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raise ValueError('No such interpolation mode "%s"' % mode)
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if mask is None:
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mask = points_3d.new_ones(1).expand(points_3d.shape[:2])
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volume_densities, volume_features = _points_to_volumes(
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points_3d,
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points_features,
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@ -6,7 +6,7 @@
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from itertools import product
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from typing import Callable, Any
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from typing import Any, Callable
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import torch
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from common_testing import get_random_cuda_device
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@ -14,6 +14,7 @@ from fvcore.common.benchmark import benchmark
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from pytorch3d.common.workaround import symeig3x3
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from test_symeig3x3 import TestSymEig3x3
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torch.set_num_threads(1)
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CUDA_DEVICE = get_random_cuda_device()
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@ -16,6 +16,7 @@ from pytorch3d.io import save_obj
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from pytorch3d.ops.iou_box3d import _box_planes, _box_triangles, box3d_overlap
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from pytorch3d.transforms.rotation_conversions import random_rotation
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OBJECTRON_TO_PYTORCH3D_FACE_IDX = [0, 4, 6, 2, 1, 5, 7, 3]
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DATA_DIR = get_tests_dir() / "data"
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DEBUG = False
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@ -12,7 +12,10 @@ from typing import Tuple
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import numpy as np
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import torch
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from common_testing import TestCaseMixin
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from pytorch3d.ops import add_pointclouds_to_volumes
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from pytorch3d.ops import (
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add_pointclouds_to_volumes,
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add_points_features_to_volume_densities_features,
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)
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from pytorch3d.ops.points_to_volumes import _points_to_volumes
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from pytorch3d.ops.sample_points_from_meshes import sample_points_from_meshes
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from pytorch3d.structures.meshes import Meshes
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@ -373,6 +376,17 @@ class TestPointsToVolumes(TestCaseMixin, unittest.TestCase):
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else:
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self.assertTrue(torch.isfinite(field.grad.data).all())
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def test_defaulted_arguments(self):
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points = torch.rand(30, 1000, 3)
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features = torch.rand(30, 1000, 5)
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_, densities = add_points_features_to_volume_densities_features(
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points,
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features,
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torch.zeros(30, 1, 32, 32, 32),
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torch.zeros(30, 5, 32, 32, 32),
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)
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self.assertClose(torch.sum(densities), torch.tensor(30 * 1000.0), atol=0.1)
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def _check_volume_slice_color_density(
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self, V, split_dim, interp_mode, clr_gt, slice_type, border=3
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):
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