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Example and test updates.

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Summary: This commit performs pulsar example and test refinements. The examples are fully adjusted to adhere to PEP style guide and additional comments are added.

Reviewed By: nikhilaravi

Differential Revision: D24723391

fbshipit-source-id: 6d289006f080140159731e7f3a8c98b582164f1a
This commit is contained in:
Christoph Lassner 2020-11-04 09:53:19 -08:00 committed by Facebook GitHub Bot
parent e9a26f263a
commit b6be3b95fb
9 changed files with 569 additions and 448 deletions
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20
docs/examples/pulsar_basic.py
View File

@ -7,14 +7,24 @@ Output: basic.png.
"""
import math
from os import path
import logging
import imageio
import torch
from pytorch3d.renderer.points.pulsar import Renderer
torch.manual_seed(1)
LOGGER = logging.getLogger(__name__)
def cli():
"""
Basic example for the pulsar sphere renderer.
Writes to `basic.png`.
"""
LOGGER.info("Rendering on GPU...")
torch.manual_seed(1)
n_points = 10
width = 1_000
height = 1_000
@ -51,5 +61,11 @@ image = renderer(
1.0e-1, # Renderer blending parameter gamma, in [1., 1e-5].
45.0, # Maximum depth.
)
print("Writing image to `%s`." % (path.abspath("basic.png")))
LOGGER.info("Writing image to `%s`.", path.abspath("basic.png"))
imageio.imsave("basic.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy())
LOGGER.info("Done.")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cli()

28
docs/examples/pulsar_basic_unified.py
View File

@ -6,10 +6,14 @@ interface for sphere renderering. It renders and saves an image with
10 random spheres.
Output: basic-pt3d.png.
"""
import logging
from os import path
import imageio
import torch
# Import `look_at_view_transform` as needed in the suggestion later in the
# example.
from pytorch3d.renderer import PerspectiveCameras # , look_at_view_transform
from pytorch3d.renderer import (
PointsRasterizationSettings,
@ -19,13 +23,21 @@ from pytorch3d.renderer import (
from pytorch3d.structures import Pointclouds
torch.manual_seed(1)
LOGGER = logging.getLogger(__name__)
def cli():
"""
Basic example for the pulsar sphere renderer using the PyTorch3D interface.
Writes to `basic-pt3d.png`.
"""
LOGGER.info("Rendering on GPU...")
torch.manual_seed(1)
n_points = 10
width = 1_000
height = 1_000
device = torch.device("cuda")
# Generate sample data.
vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 10.0
vert_pos[:, 2] += 25.0
@ -63,5 +75,13 @@ image = renderer(
radius_world=True,
bg_col=torch.ones((3,), dtype=torch.float32, device=device),
)[0]
print("Writing image to `%s`." % (path.abspath("basic-pt3d.png")))
imageio.imsave("basic-pt3d.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy())
LOGGER.info("Writing image to `%s`.", path.abspath("basic-pt3d.png"))
imageio.imsave(
"basic-pt3d.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy()
)
LOGGER.info("Done.")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cli()

39
docs/examples/pulsar_cam.py
View File

@ -9,6 +9,7 @@ distorted. Gradient-based optimization is used to converge towards the
original camera parameters.
Output: cam.gif.
"""
import logging
import math
from os import path
@ -21,10 +22,11 @@ from pytorch3d.transforms import axis_angle_to_matrix, matrix_to_rotation_6d
from torch import nn, optim
n_points = 20
width = 1_000
height = 1_000
device = torch.device("cuda")
LOGGER = logging.getLogger(__name__)
N_POINTS = 20
WIDTH = 1_000
HEIGHT = 1_000
DEVICE = torch.device("cuda")
class SceneModel(nn.Module):
@ -45,20 +47,20 @@ class SceneModel(nn.Module):
self.gamma = 0.1
# Points.
torch.manual_seed(1)
vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0
vert_pos[:, 2] += 25.0
vert_pos[:, :2] -= 5.0
self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False))
self.register_parameter(
"vert_col",
nn.Parameter(
torch.rand(n_points, 3, dtype=torch.float32), requires_grad=False
torch.rand(N_POINTS, 3, dtype=torch.float32), requires_grad=False
),
)
self.register_parameter(
"vert_rad",
nn.Parameter(
torch.rand(n_points, dtype=torch.float32), requires_grad=False
torch.rand(N_POINTS, dtype=torch.float32), requires_grad=False
),
)
self.register_parameter(
@ -90,7 +92,7 @@ class SceneModel(nn.Module):
torch.tensor([4.8, 1.8], dtype=torch.float32), requires_grad=True
),
)
self.renderer = Renderer(width, height, n_points, right_handed_system=True)
self.renderer = Renderer(WIDTH, HEIGHT, N_POINTS, right_handed_system=True)
def forward(self):
return self.renderer.forward(
@ -103,6 +105,13 @@ class SceneModel(nn.Module):
)
def cli():
"""
Camera optimization example using pulsar.
Writes to `cam.gif`.
"""
LOGGER.info("Loading reference...")
# Load reference.
ref = (
torch.from_numpy(
@ -111,9 +120,9 @@ ref = (
)[:, ::-1, :].copy()
).to(torch.float32)
/ 255.0
).to(device)
).to(DEVICE)
# Set up model.
model = SceneModel().to(device)
model = SceneModel().to(DEVICE)
# Optimizer.
optimizer = optim.SGD(
[
@ -123,7 +132,7 @@ optimizer = optim.SGD(
]
)
print("Writing video to `%s`." % (path.abspath("cam.gif")))
LOGGER.info("Writing video to `%s`.", path.abspath("cam.gif"))
writer = imageio.get_writer("cam.gif", format="gif", fps=25)
# Optimize.
@ -154,7 +163,13 @@ for i in range(300):
cv2.waitKey(1)
# Update.
loss = ((result - ref) ** 2).sum()
print("loss {}: {}".format(i, loss.item()))
LOGGER.info("loss %d: %f", i, loss.item())
loss.backward()
optimizer.step()
writer.close()
LOGGER.info("Done.")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cli()

48
docs/examples/pulsar_cam_unified.py
View File

@ -10,11 +10,15 @@ original camera parameters.
Output: cam-pt3d.gif
"""
from os import path
import logging
import cv2
import imageio
import numpy as np
import torch
# Import `look_at_view_transform` as needed in the suggestion later in the
# example.
from pytorch3d.renderer.cameras import PerspectiveCameras # , look_at_view_transform
from pytorch3d.renderer.points import (
PointsRasterizationSettings,
@ -26,10 +30,11 @@ from pytorch3d.transforms import axis_angle_to_matrix
from torch import nn, optim
n_points = 20
width = 1_000
height = 1_000
device = torch.device("cuda")
LOGGER = logging.getLogger(__name__)
N_POINTS = 20
WIDTH = 1_000
HEIGHT = 1_000
DEVICE = torch.device("cuda")
class SceneModel(nn.Module):
@ -50,21 +55,21 @@ class SceneModel(nn.Module):
self.gamma = 0.1
# Points.
torch.manual_seed(1)
vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0
vert_pos[:, 2] += 25.0
vert_pos[:, :2] -= 5.0
self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False))
self.register_parameter(
"vert_col",
nn.Parameter(
torch.rand(n_points, 3, dtype=torch.float32),
torch.rand(N_POINTS, 3, dtype=torch.float32),
requires_grad=False,
),
)
self.register_parameter(
"vert_rad",
nn.Parameter(
torch.rand(n_points, dtype=torch.float32),
torch.rand(N_POINTS, dtype=torch.float32),
requires_grad=False,
),
)
@ -118,11 +123,11 @@ class SceneModel(nn.Module):
focal_length=self.focal_length,
R=self.cam_rot[None, ...],
T=self.cam_pos[None, ...],
image_size=((width, height),),
device=device,
image_size=((WIDTH, HEIGHT),),
device=DEVICE,
)
raster_settings = PointsRasterizationSettings(
image_size=(width, height),
image_size=(WIDTH, HEIGHT),
radius=self.vert_rad,
)
rasterizer = PointsRasterizer(
@ -142,7 +147,7 @@ class SceneModel(nn.Module):
zfar=(45.0,),
znear=(1.0,),
radius_world=True,
bg_col=torch.ones((3,), dtype=torch.float32, device=device),
bg_col=torch.ones((3,), dtype=torch.float32, device=DEVICE),
# As mentioned above: workaround for device placement of gradients for
# camera parameters.
focal_length=self.focal_length,
@ -151,6 +156,13 @@ class SceneModel(nn.Module):
)[0]
def cli():
"""
Camera optimization example using pulsar.
Writes to `cam.gif`.
"""
LOGGER.info("Loading reference...")
# Load reference.
ref = (
torch.from_numpy(
@ -159,9 +171,9 @@ ref = (
)[:, ::-1, :].copy()
).to(torch.float32)
/ 255.0
).to(device)
).to(DEVICE)
# Set up model.
model = SceneModel().to(device)
model = SceneModel().to(DEVICE)
# Optimizer.
optimizer = optim.SGD(
[
@ -173,7 +185,7 @@ optimizer = optim.SGD(
]
)
print("Writing video to `%s`." % (path.abspath("cam-pt3d.gif")))
LOGGER.info("Writing video to `%s`.", path.abspath("cam-pt3d.gif"))
writer = imageio.get_writer("cam-pt3d.gif", format="gif", fps=25)
# Optimize.
@ -204,7 +216,13 @@ for i in range(300):
cv2.waitKey(1)
# Update.
loss = ((result - ref) ** 2).sum()
print("loss {}: {}".format(i, loss.item()))
LOGGER.info("loss %d: %f", i, loss.item())
loss.backward()
optimizer.step()
writer.close()
LOGGER.info("Done.")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cli()

53
docs/examples/pulsar_multiview.py
View File

@ -3,7 +3,8 @@
"""
This example demonstrates multiview 3D reconstruction using the plain
pulsar interface. For this, reference images have been pre-generated
(you can find them at `../../tests/pulsar/reference/examples_TestRenderer_test_multiview_%d.png`).
(you can find them at
`../../tests/pulsar/reference/examples_TestRenderer_test_multiview_%d.png`).
The camera parameters are assumed given. The scene is initialized with
random spheres. Gradient-based optimization is used to optimize sphere
parameters and prune spheres to converge to a 3D representation.
@ -14,6 +15,7 @@ structures yet.
"""
import math
from os import path
import logging
import cv2
import imageio
@ -23,11 +25,12 @@ from pytorch3d.renderer.points.pulsar import Renderer
from torch import nn, optim
n_points = 400_000
width = 1_000
height = 1_000
visualize_ids = [0, 1]
device = torch.device("cuda")
LOGGER = logging.getLogger(__name__)
N_POINTS = 400_000
WIDTH = 1_000
HEIGHT = 1_000
VISUALIZE_IDS = [0, 1]
DEVICE = torch.device("cuda")
class SceneModel(nn.Module):
@ -50,27 +53,27 @@ class SceneModel(nn.Module):
self.gamma = 1.0
# Points.
torch.manual_seed(1)
vert_pos = torch.rand((1, n_points, 3), dtype=torch.float32) * 10.0
vert_pos = torch.rand((1, N_POINTS, 3), dtype=torch.float32) * 10.0
vert_pos[:, :, 2] += 25.0
vert_pos[:, :, :2] -= 5.0
self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True))
self.register_parameter(
"vert_col",
nn.Parameter(
torch.ones(1, n_points, 3, dtype=torch.float32) * 0.5,
torch.ones(1, N_POINTS, 3, dtype=torch.float32) * 0.5,
requires_grad=True,
),
)
self.register_parameter(
"vert_rad",
nn.Parameter(
torch.ones(1, n_points, dtype=torch.float32) * 0.05, requires_grad=True
torch.ones(1, N_POINTS, dtype=torch.float32) * 0.05, requires_grad=True
),
)
self.register_parameter(
"vert_opy",
nn.Parameter(
torch.ones(1, n_points, dtype=torch.float32), requires_grad=True
torch.ones(1, N_POINTS, dtype=torch.float32), requires_grad=True
),
)
self.register_buffer(
@ -92,7 +95,7 @@ class SceneModel(nn.Module):
dtype=torch.float32,
),
)
self.renderer = Renderer(width, height, n_points, right_handed_system=True)
self.renderer = Renderer(WIDTH, HEIGHT, N_POINTS, right_handed_system=True)
def forward(self, cam=None):
if cam is None:
@ -110,6 +113,16 @@ class SceneModel(nn.Module):
)
def cli():
"""
Simple demonstration for a multi-view 3D reconstruction using pulsar.
This example makes use of opacity, which is not yet supported through
the unified PyTorch3D interface.
Writes to `multiview.gif`.
"""
LOGGER.info("Loading reference...")
# Load reference.
ref = torch.stack(
[
@ -122,9 +135,9 @@ ref = torch.stack(
/ 255.0
for idx in range(8)
]
).to(device)
).to(DEVICE)
# Set up model.
model = SceneModel().to(device)
model = SceneModel().to(DEVICE)
# Optimizer.
optimizer = optim.SGD(
[
@ -136,7 +149,7 @@ optimizer = optim.SGD(
# For visualization.
angle = 0.0
print("Writing video to `%s`." % (path.abspath("multiview.avi")))
LOGGER.info("Writing video to `%s`.", path.abspath("multiview.avi"))
writer = imageio.get_writer("multiview.gif", format="gif", fps=25)
# Optimize.
@ -166,7 +179,7 @@ for i in range(300):
cv2.waitKey(1)
# Update.
loss = ((result - ref) ** 2).sum()
print("loss {}: {}".format(i, loss.item()))
LOGGER.info("loss %d: %f", i, loss.item())
loss.backward()
optimizer.step()
# Cleanup.
@ -176,7 +189,7 @@ for i in range(300):
model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0
model.vert_rad.data[model.vert_rad < 0.001] = 0.0001
vd = (
(model.vert_col - torch.ones(1, 1, 3, dtype=torch.float32).to(device))
(model.vert_col - torch.ones(1, 1, 3, dtype=torch.float32).to(DEVICE))
.abs()
.sum(dim=2)
)
@ -196,7 +209,7 @@ for i in range(300):
]
],
dtype=torch.float32,
).to(device)
).to(DEVICE)
with torch.no_grad():
result = model.forward(cam=cam_control)[0]
result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
@ -204,3 +217,9 @@ for i in range(300):
writer.append_data(result_im)
angle += 0.05
writer.close()
LOGGER.info("Done.")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cli()

39
docs/examples/pulsar_optimization.py
View File

@ -9,6 +9,7 @@ optimization is used to converge towards a faithful
scene representation.
"""
import math
import logging
import cv2
import imageio
@ -18,10 +19,11 @@ from pytorch3d.renderer.points.pulsar import Renderer
from torch import nn, optim
n_points = 10_000
width = 1_000
height = 1_000
device = torch.device("cuda")
LOGGER = logging.getLogger(__name__)
N_POINTS = 10_000
WIDTH = 1_000
HEIGHT = 1_000
DEVICE = torch.device("cuda")
class SceneModel(nn.Module):
@ -42,20 +44,20 @@ class SceneModel(nn.Module):
self.gamma = 1.0
# Points.
torch.manual_seed(1)
vert_pos = torch.rand(n_points, 3, dtype=torch.float32) * 10.0
vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0
vert_pos[:, 2] += 25.0
vert_pos[:, :2] -= 5.0
self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True))
self.register_parameter(
"vert_col",
nn.Parameter(
torch.ones(n_points, 3, dtype=torch.float32) * 0.5, requires_grad=True
torch.ones(N_POINTS, 3, dtype=torch.float32) * 0.5, requires_grad=True
),
)
self.register_parameter(
"vert_rad",
nn.Parameter(
torch.ones(n_points, dtype=torch.float32) * 0.3, requires_grad=True
torch.ones(N_POINTS, dtype=torch.float32) * 0.3, requires_grad=True
),
)
self.register_buffer(
@ -67,7 +69,7 @@ class SceneModel(nn.Module):
# The volumetric optimization works better with a higher number of tracked
# intersections per ray.
self.renderer = Renderer(
width, height, n_points, n_track=32, right_handed_system=True
WIDTH, HEIGHT, N_POINTS, n_track=32, right_handed_system=True
)
def forward(self):
@ -82,6 +84,11 @@ class SceneModel(nn.Module):
)
def cli():
"""
Scene optimization example using pulsar.
"""
LOGGER.info("Loading reference...")
# Load reference.
ref = (
torch.from_numpy(
@ -90,9 +97,9 @@ ref = (
)[:, ::-1, :].copy()
).to(torch.float32)
/ 255.0
).to(device)
).to(DEVICE)
# Set up model.
model = SceneModel().to(device)
model = SceneModel().to(DEVICE)
# Optimizer.
optimizer = optim.SGD(
[
@ -101,7 +108,7 @@ optimizer = optim.SGD(
{"params": [model.vert_pos], "lr": 1e-2},
]
)
LOGGER.info("Optimizing...")
# Optimize.
for i in range(500):
optimizer.zero_grad()
@ -129,7 +136,7 @@ for i in range(500):
cv2.waitKey(1)
# Update.
loss = ((result - ref) ** 2).sum()
print("loss {}: {}".format(i, loss.item()))
LOGGER.info("loss %d: %f", i, loss.item())
loss.backward()
optimizer.step()
# Cleanup.
@ -139,8 +146,14 @@ for i in range(500):
model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0
model.vert_rad.data[model.vert_rad < 0.001] = 0.0001
vd = (
(model.vert_col - torch.ones(3, dtype=torch.float32).to(device))
(model.vert_col - torch.ones(3, dtype=torch.float32).to(DEVICE))
.abs()
.sum(dim=1)
)
model.vert_pos.data[vd <= 0.2] = -1000.0
LOGGER.info("Done.")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cli()

52
docs/examples/pulsar_optimization_unified.py
View File

@ -9,11 +9,15 @@ optimization is used to converge towards a faithful
scene representation.
"""
import math
import logging
import cv2
import imageio
import numpy as np
import torch
# Import `look_at_view_transform` as needed in the suggestion later in the
# example.
from pytorch3d.renderer.cameras import PerspectiveCameras # , look_at_view_transform
from pytorch3d.renderer.points import (
PointsRasterizationSettings,
@ -24,10 +28,11 @@ from pytorch3d.structures.pointclouds import Pointclouds
from torch import nn, optim
n_points = 10_000
width = 1_000
height = 1_000
device = torch.device("cuda")
LOGGER = logging.getLogger(__name__)
N_POINTS = 10_000
WIDTH = 1_000
HEIGHT = 1_000
DEVICE = torch.device("cuda")
class SceneModel(nn.Module):
@ -48,21 +53,21 @@ class SceneModel(nn.Module):
self.gamma = 1.0
# Points.
torch.manual_seed(1)
vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 10.0
vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32, device=DEVICE) * 10.0
vert_pos[:, 2] += 25.0
vert_pos[:, :2] -= 5.0
self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True))
self.register_parameter(
"vert_col",
nn.Parameter(
torch.ones(n_points, 3, dtype=torch.float32, device=device) * 0.5,
torch.ones(N_POINTS, 3, dtype=torch.float32, device=DEVICE) * 0.5,
requires_grad=True,
),
)
self.register_parameter(
"vert_rad",
nn.Parameter(
torch.ones(n_points, dtype=torch.float32) * 0.3, requires_grad=True
torch.ones(N_POINTS, dtype=torch.float32) * 0.3, requires_grad=True
),
)
self.register_buffer(
@ -77,13 +82,13 @@ class SceneModel(nn.Module):
# sensor width (see the pulsar example). This means we need here
# 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar.
focal_length=5.0,
R=torch.eye(3, dtype=torch.float32, device=device)[None, ...],
T=torch.zeros((1, 3), dtype=torch.float32, device=device),
image_size=((width, height),),
device=device,
R=torch.eye(3, dtype=torch.float32, device=DEVICE)[None, ...],
T=torch.zeros((1, 3), dtype=torch.float32, device=DEVICE),
image_size=((WIDTH, HEIGHT),),
device=DEVICE,
)
raster_settings = PointsRasterizationSettings(
image_size=(width, height),
image_size=(WIDTH, HEIGHT),
radius=self.vert_rad,
)
rasterizer = PointsRasterizer(
@ -103,10 +108,15 @@ class SceneModel(nn.Module):
zfar=(45.0,),
znear=(1.0,),
radius_world=True,
bg_col=torch.ones((3,), dtype=torch.float32, device=device),
bg_col=torch.ones((3,), dtype=torch.float32, device=DEVICE),
)[0]
def cli():
"""
Scene optimization example using pulsar and the unified PyTorch3D interface.
"""
LOGGER.info("Loading reference...")
# Load reference.
ref = (
torch.from_numpy(
@ -115,9 +125,9 @@ ref = (
)[:, ::-1, :].copy()
).to(torch.float32)
/ 255.0
).to(device)
).to(DEVICE)
# Set up model.
model = SceneModel().to(device)
model = SceneModel().to(DEVICE)
# Optimizer.
optimizer = optim.SGD(
[
@ -126,7 +136,7 @@ optimizer = optim.SGD(
{"params": [model.vert_pos], "lr": 1e-2},
]
)
LOGGER.info("Optimizing...")
# Optimize.
for i in range(500):
optimizer.zero_grad()
@ -154,7 +164,7 @@ for i in range(500):
cv2.waitKey(1)
# Update.
loss = ((result - ref) ** 2).sum()
print("loss {}: {}".format(i, loss.item()))
LOGGER.info("loss %d: %f", i, loss.item())
loss.backward()
optimizer.step()
# Cleanup.
@ -164,8 +174,14 @@ for i in range(500):
model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0
model.vert_rad.data[model.vert_rad < 0.001] = 0.0001
vd = (
(model.vert_col - torch.ones(3, dtype=torch.float32).to(device))
(model.vert_col - torch.ones(3, dtype=torch.float32).to(DEVICE))
.abs()
.sum(dim=1)
)
model.vert_pos.data[vd <= 0.2] = -1000.0
LOGGER.info("Done.")
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
cli()

4
tests/pulsar/test_depth.py
View File

@ -44,6 +44,8 @@ class TestDepth(TestCaseMixin, unittest.TestCase):
n_channels=1,
).to(device)
data = torch.load(IN_REF_FP, map_location="cpu")
# For creating the reference files.
# Use in case of updates.
# data["pos"] = torch.rand_like(data["pos"])
# data["pos"][:, 0] = data["pos"][:, 0] * 2. - 1.
# data["pos"][:, 1] = data["pos"][:, 1] * 2. - 1.
@ -74,6 +76,8 @@ class TestDepth(TestCaseMixin, unittest.TestCase):
),
depth_vis.cpu().numpy().astype(np.uint8),
)
# For creating the reference files.
# Use in case of updates.
# torch.save(
# data, path.join(path.dirname(__file__), "reference", "nr0000-in.pth")
# )

2
tests/pulsar/test_small_spheres.py
View File

@ -123,7 +123,7 @@ class TestSmallSpheres(unittest.TestCase):
self.assertTrue(
(sphere_ids == idx).sum() > 0, "Sphere ID %d missing!" % (idx)
)
# Visualize.
# Visualization code. Activate for debugging.
# result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
# cv2.imshow("res", result_im[0, :, :, ::-1])
# cv2.waitKey(0)