Spaces:
Running
Running
File size: 9,171 Bytes
7088d16 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import pytorch3d as pt3d
import torch
from pytorch3d.implicitron.models.view_pooler.view_sampler import ViewSampler
from pytorch3d.implicitron.tools.config import expand_args_fields
class TestViewsampling(unittest.TestCase):
def setUp(self):
torch.manual_seed(42)
expand_args_fields(ViewSampler)
def _init_view_sampler_problem(self, random_masks):
"""
Generates a view-sampling problem:
- 4 source views, 1st/2nd from the first sequence 'seq1', the rest from 'seq2'
- 3 sets of 3D points from sequences 'seq1', 'seq2', 'seq2' respectively.
- first 50 points in each batch correctly project to the source views,
while the remaining 50 do not land in any projection plane.
- each source view is labeled with image feature tensors of shape 7x100x50,
where all elements of the n-th tensor are set to `n+1`.
- the elements of the source view masks are either set to random binary number
(if `random_masks==True`), or all set to 1 (`random_masks==False`).
- the source view cameras are uniformly distributed on a unit circle
in the x-z plane and look at (0,0,0).
"""
seq_id_camera = ["seq1", "seq1", "seq2", "seq2"]
seq_id_pts = ["seq1", "seq2", "seq2"]
pts_batch = 3
n_pts = 100
n_views = 4
fdim = 7
H = 100
W = 50
# points that land into the projection planes of all cameras
pts_inside = (
torch.nn.functional.normalize(
torch.randn(pts_batch, n_pts // 2, 3, device="cuda"),
dim=-1,
)
* 0.1
)
# move the outside points far above the scene
pts_outside = pts_inside.clone()
pts_outside[:, :, 1] += 1e8
pts = torch.cat([pts_inside, pts_outside], dim=1)
R, T = pt3d.renderer.look_at_view_transform(
dist=1.0,
elev=0.0,
azim=torch.linspace(0, 360, n_views + 1)[:n_views],
degrees=True,
device=pts.device,
)
focal_length = R.new_ones(n_views, 2)
principal_point = R.new_zeros(n_views, 2)
camera = pt3d.renderer.PerspectiveCameras(
R=R,
T=T,
focal_length=focal_length,
principal_point=principal_point,
device=pts.device,
)
feats_map = torch.arange(n_views, device=pts.device, dtype=pts.dtype) + 1
feats = {"feats": feats_map[:, None, None, None].repeat(1, fdim, H, W)}
masks = (
torch.rand(n_views, 1, H, W, device=pts.device, dtype=pts.dtype) > 0.5
).type_as(R)
if not random_masks:
masks[:] = 1.0
return pts, camera, feats, masks, seq_id_camera, seq_id_pts
def test_compare_with_naive(self):
"""
Compares the outputs of the efficient ViewSampler module with a
naive implementation.
"""
(
pts,
camera,
feats,
masks,
seq_id_camera,
seq_id_pts,
) = self._init_view_sampler_problem(True)
for masked_sampling in (True, False):
feats_sampled_n, masks_sampled_n = _view_sample_naive(
pts,
seq_id_pts,
camera,
seq_id_camera,
feats,
masks,
masked_sampling,
)
# make sure we generate the constructor for ViewSampler
expand_args_fields(ViewSampler)
view_sampler = ViewSampler(masked_sampling=masked_sampling)
feats_sampled, masks_sampled = view_sampler(
pts=pts,
seq_id_pts=seq_id_pts,
camera=camera,
seq_id_camera=seq_id_camera,
feats=feats,
masks=masks,
)
for k in feats_sampled.keys():
self.assertTrue(torch.allclose(feats_sampled[k], feats_sampled_n[k]))
self.assertTrue(torch.allclose(masks_sampled, masks_sampled_n))
def test_viewsampling(self):
"""
Generates a viewsampling problem with predictable outcome, and compares
the ViewSampler's output to the expected result.
"""
(
pts,
camera,
feats,
masks,
seq_id_camera,
seq_id_pts,
) = self._init_view_sampler_problem(False)
expand_args_fields(ViewSampler)
for masked_sampling in (True, False):
view_sampler = ViewSampler(masked_sampling=masked_sampling)
feats_sampled, masks_sampled = view_sampler(
pts=pts,
seq_id_pts=seq_id_pts,
camera=camera,
seq_id_camera=seq_id_camera,
feats=feats,
masks=masks,
)
n_views = camera.R.shape[0]
n_pts = pts.shape[1]
feat_dim = feats["feats"].shape[1]
pts_batch = pts.shape[0]
n_pts_away = n_pts // 2
for pts_i in range(pts_batch):
for view_i in range(n_views):
if seq_id_pts[pts_i] != seq_id_camera[view_i]:
# points / cameras come from different sequences
gt_masks = pts.new_zeros(n_pts, 1)
gt_feats = pts.new_zeros(n_pts, feat_dim)
else:
gt_masks = pts.new_ones(n_pts, 1)
gt_feats = pts.new_ones(n_pts, feat_dim) * (view_i + 1)
gt_feats[n_pts_away:] = 0.0
if masked_sampling:
gt_masks[n_pts_away:] = 0.0
for k in feats_sampled:
self.assertTrue(
torch.allclose(
feats_sampled[k][pts_i, view_i],
gt_feats,
)
)
self.assertTrue(
torch.allclose(
masks_sampled[pts_i, view_i],
gt_masks,
)
)
def _view_sample_naive(
pts,
seq_id_pts,
camera,
seq_id_camera,
feats,
masks,
masked_sampling,
):
"""
A naive implementation of the forward pass of ViewSampler.
Refer to ViewSampler's docstring for description of the arguments.
"""
pts_batch = pts.shape[0]
n_views = camera.R.shape[0]
n_pts = pts.shape[1]
feats_sampled = [[[] for _ in range(n_views)] for _ in range(pts_batch)]
masks_sampled = [[[] for _ in range(n_views)] for _ in range(pts_batch)]
for pts_i in range(pts_batch):
for view_i in range(n_views):
if seq_id_pts[pts_i] != seq_id_camera[view_i]:
# points/cameras come from different sequences
feats_sampled_ = {
k: f.new_zeros(n_pts, f.shape[1]) for k, f in feats.items()
}
masks_sampled_ = masks.new_zeros(n_pts, 1)
else:
# same sequence of pts and cameras -> sample
feats_sampled_, masks_sampled_ = _sample_one_view_naive(
camera[view_i],
pts[pts_i],
{k: f[view_i] for k, f in feats.items()},
masks[view_i],
masked_sampling,
sampling_mode="bilinear",
)
feats_sampled[pts_i][view_i] = feats_sampled_
masks_sampled[pts_i][view_i] = masks_sampled_
masks_sampled_cat = torch.stack([torch.stack(m) for m in masks_sampled])
feats_sampled_cat = {}
for k in feats_sampled[0][0].keys():
feats_sampled_cat[k] = torch.stack(
[torch.stack([f_[k] for f_ in f]) for f in feats_sampled]
)
return feats_sampled_cat, masks_sampled_cat
def _sample_one_view_naive(
camera,
pts,
feats,
masks,
masked_sampling,
sampling_mode="bilinear",
):
"""
Sample a single source view.
"""
proj_ndc = camera.transform_points(pts[None])[None, ..., :-1] # 1 x 1 x n_pts x 2
feats_sampled = {
k: pt3d.renderer.ndc_grid_sample(f[None], proj_ndc, mode=sampling_mode).permute(
0, 3, 1, 2
)[0, :, :, 0]
for k, f in feats.items()
} # n_pts x dim
if not masked_sampling:
n_pts = pts.shape[0]
masks_sampled = proj_ndc.new_ones(n_pts, 1)
else:
masks_sampled = pt3d.renderer.ndc_grid_sample(
masks[None],
proj_ndc,
mode=sampling_mode,
align_corners=False,
)[0, 0, 0, :][:, None]
return feats_sampled, masks_sampled
|