mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
59972b121d
Summary: Modified the compositor background color tests to account for either a 3rd or 4th channel. Also replaced hard coding of channel value with C. Implemented changes to alpha channel appending logic, and cleaned up extraneous warnings and checks, per task instructions. Fixes https://github.com/facebookresearch/pytorch3d/issues/1048 Reviewed By: bottler Differential Revision: D34305312 fbshipit-source-id: 2176c3bdd897d1a2ba6ff4c6fa801fea889e4f02
425 lines
17 KiB
Python
425 lines
17 KiB
Python
# 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.
|
|
|
|
|
|
"""
|
|
Sanity checks for output images from the pointcloud renderer.
|
|
"""
|
|
import unittest
|
|
import warnings
|
|
from os import path
|
|
|
|
import numpy as np
|
|
import torch
|
|
from common_testing import (
|
|
TestCaseMixin,
|
|
get_pytorch3d_dir,
|
|
get_tests_dir,
|
|
load_rgb_image,
|
|
)
|
|
from PIL import Image
|
|
from pytorch3d.renderer.cameras import (
|
|
FoVOrthographicCameras,
|
|
FoVPerspectiveCameras,
|
|
OrthographicCameras,
|
|
PerspectiveCameras,
|
|
look_at_view_transform,
|
|
)
|
|
from pytorch3d.renderer.compositing import alpha_composite, norm_weighted_sum
|
|
from pytorch3d.renderer.points import (
|
|
AlphaCompositor,
|
|
NormWeightedCompositor,
|
|
PointsRasterizationSettings,
|
|
PointsRasterizer,
|
|
PointsRenderer,
|
|
PulsarPointsRenderer,
|
|
)
|
|
from pytorch3d.structures.pointclouds import Pointclouds
|
|
from pytorch3d.utils.ico_sphere import ico_sphere
|
|
|
|
|
|
# If DEBUG=True, save out images generated in the tests for debugging.
|
|
# All saved images have prefix DEBUG_
|
|
DEBUG = False
|
|
DATA_DIR = get_tests_dir() / "data"
|
|
|
|
|
|
class TestRenderPoints(TestCaseMixin, unittest.TestCase):
|
|
def test_simple_sphere(self):
|
|
device = torch.device("cuda:0")
|
|
sphere_mesh = ico_sphere(1, device)
|
|
verts_padded = sphere_mesh.verts_padded()
|
|
# Shift vertices to check coordinate frames are correct.
|
|
verts_padded[..., 1] += 0.2
|
|
verts_padded[..., 0] += 0.2
|
|
pointclouds = Pointclouds(
|
|
points=verts_padded, features=torch.ones_like(verts_padded)
|
|
)
|
|
R, T = look_at_view_transform(2.7, 0.0, 0.0)
|
|
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
|
|
raster_settings = PointsRasterizationSettings(
|
|
image_size=256, radius=5e-2, points_per_pixel=1
|
|
)
|
|
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
|
|
compositor = NormWeightedCompositor()
|
|
renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)
|
|
|
|
# Load reference image
|
|
filename = "simple_pointcloud_sphere.png"
|
|
image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
|
|
|
|
for bin_size in [0, None]:
|
|
# Check both naive and coarse to fine produce the same output.
|
|
renderer.rasterizer.raster_settings.bin_size = bin_size
|
|
images = renderer(pointclouds)
|
|
rgb = images[0, ..., :3].squeeze().cpu()
|
|
if DEBUG:
|
|
filename = "DEBUG_%s" % filename
|
|
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
|
|
DATA_DIR / filename
|
|
)
|
|
self.assertClose(rgb, image_ref)
|
|
|
|
def test_simple_sphere_pulsar(self):
|
|
for device in [torch.device("cpu"), torch.device("cuda")]:
|
|
sphere_mesh = ico_sphere(1, device)
|
|
verts_padded = sphere_mesh.verts_padded()
|
|
# Shift vertices to check coordinate frames are correct.
|
|
verts_padded[..., 1] += 0.2
|
|
verts_padded[..., 0] += 0.2
|
|
pointclouds = Pointclouds(
|
|
points=verts_padded, features=torch.ones_like(verts_padded)
|
|
)
|
|
for azimuth in [0.0, 90.0]:
|
|
R, T = look_at_view_transform(2.7, 0.0, azimuth)
|
|
for camera_name, cameras in [
|
|
("fovperspective", FoVPerspectiveCameras(device=device, R=R, T=T)),
|
|
(
|
|
"fovorthographic",
|
|
FoVOrthographicCameras(device=device, R=R, T=T),
|
|
),
|
|
("perspective", PerspectiveCameras(device=device, R=R, T=T)),
|
|
("orthographic", OrthographicCameras(device=device, R=R, T=T)),
|
|
]:
|
|
raster_settings = PointsRasterizationSettings(
|
|
image_size=256, radius=5e-2, points_per_pixel=1
|
|
)
|
|
rasterizer = PointsRasterizer(
|
|
cameras=cameras, raster_settings=raster_settings
|
|
)
|
|
renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device)
|
|
# Load reference image
|
|
filename = (
|
|
"pulsar_simple_pointcloud_sphere_"
|
|
f"azimuth{azimuth}_{camera_name}.png"
|
|
)
|
|
image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
|
|
images = renderer(
|
|
pointclouds, gamma=(1e-3,), znear=(1.0,), zfar=(100.0,)
|
|
)
|
|
rgb = images[0, ..., :3].squeeze().cpu()
|
|
if DEBUG:
|
|
filename = "DEBUG_%s" % filename
|
|
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
|
|
DATA_DIR / filename
|
|
)
|
|
self.assertClose(rgb, image_ref, rtol=7e-3, atol=5e-3)
|
|
|
|
def test_unified_inputs_pulsar(self):
|
|
# Test data on different devices.
|
|
for device in [torch.device("cpu"), torch.device("cuda")]:
|
|
sphere_mesh = ico_sphere(1, device)
|
|
verts_padded = sphere_mesh.verts_padded()
|
|
pointclouds = Pointclouds(
|
|
points=verts_padded, features=torch.ones_like(verts_padded)
|
|
)
|
|
R, T = look_at_view_transform(2.7, 0.0, 0.0)
|
|
# Test the different camera types.
|
|
for _, cameras in [
|
|
("fovperspective", FoVPerspectiveCameras(device=device, R=R, T=T)),
|
|
(
|
|
"fovorthographic",
|
|
FoVOrthographicCameras(device=device, R=R, T=T),
|
|
),
|
|
("perspective", PerspectiveCameras(device=device, R=R, T=T)),
|
|
("orthographic", OrthographicCameras(device=device, R=R, T=T)),
|
|
]:
|
|
# Test different ways for image size specification.
|
|
for image_size in (256, (256, 256)):
|
|
raster_settings = PointsRasterizationSettings(
|
|
image_size=image_size, radius=5e-2, points_per_pixel=1
|
|
)
|
|
rasterizer = PointsRasterizer(
|
|
cameras=cameras, raster_settings=raster_settings
|
|
)
|
|
# Test that the compositor can be provided. It's value is ignored
|
|
# so use a dummy.
|
|
_ = PulsarPointsRenderer(rasterizer=rasterizer, compositor=1).to(
|
|
device
|
|
)
|
|
# Constructor without compositor.
|
|
_ = PulsarPointsRenderer(rasterizer=rasterizer).to(device)
|
|
# Constructor with n_channels.
|
|
_ = PulsarPointsRenderer(rasterizer=rasterizer, n_channels=3).to(
|
|
device
|
|
)
|
|
# Constructor with max_num_spheres.
|
|
renderer = PulsarPointsRenderer(
|
|
rasterizer=rasterizer, max_num_spheres=1000
|
|
).to(device)
|
|
# Test the forward function.
|
|
if isinstance(cameras, (PerspectiveCameras, OrthographicCameras)):
|
|
# znear and zfar is required in this case.
|
|
self.assertRaises(
|
|
ValueError,
|
|
lambda: renderer.forward(
|
|
point_clouds=pointclouds, gamma=(1e-4,)
|
|
),
|
|
)
|
|
renderer.forward(
|
|
point_clouds=pointclouds,
|
|
gamma=(1e-4,),
|
|
znear=(1.0,),
|
|
zfar=(2.0,),
|
|
)
|
|
# znear and zfar must be batched.
|
|
self.assertRaises(
|
|
TypeError,
|
|
lambda: renderer.forward(
|
|
point_clouds=pointclouds,
|
|
gamma=(1e-4,),
|
|
znear=1.0,
|
|
zfar=(2.0,),
|
|
),
|
|
)
|
|
self.assertRaises(
|
|
TypeError,
|
|
lambda: renderer.forward(
|
|
point_clouds=pointclouds,
|
|
gamma=(1e-4,),
|
|
znear=(1.0,),
|
|
zfar=2.0,
|
|
),
|
|
)
|
|
else:
|
|
# gamma must be batched.
|
|
self.assertRaises(
|
|
TypeError,
|
|
lambda: renderer.forward(
|
|
point_clouds=pointclouds, gamma=1e-4
|
|
),
|
|
)
|
|
renderer.forward(point_clouds=pointclouds, gamma=(1e-4,))
|
|
# rasterizer width and height change.
|
|
renderer.rasterizer.raster_settings.image_size = 0
|
|
self.assertRaises(
|
|
ValueError,
|
|
lambda: renderer.forward(
|
|
point_clouds=pointclouds, gamma=(1e-4,)
|
|
),
|
|
)
|
|
|
|
def test_pointcloud_with_features(self):
|
|
device = torch.device("cuda:0")
|
|
file_dir = get_pytorch3d_dir() / "docs/tutorials/data"
|
|
pointcloud_filename = file_dir / "PittsburghBridge/pointcloud.npz"
|
|
|
|
# Note, this file is too large to check in to the repo.
|
|
# Download the file to run the test locally.
|
|
if not path.exists(pointcloud_filename):
|
|
url = (
|
|
"https://dl.fbaipublicfiles.com/pytorch3d/data/"
|
|
"PittsburghBridge/pointcloud.npz"
|
|
)
|
|
msg = (
|
|
"pointcloud.npz not found, download from %s, save it at the path %s, and rerun"
|
|
% (url, pointcloud_filename)
|
|
)
|
|
warnings.warn(msg)
|
|
return True
|
|
|
|
# Load point cloud
|
|
pointcloud = np.load(pointcloud_filename)
|
|
verts = torch.Tensor(pointcloud["verts"]).to(device)
|
|
rgb_feats = torch.Tensor(pointcloud["rgb"]).to(device)
|
|
|
|
verts.requires_grad = True
|
|
rgb_feats.requires_grad = True
|
|
point_cloud = Pointclouds(points=[verts], features=[rgb_feats])
|
|
|
|
R, T = look_at_view_transform(20, 10, 0)
|
|
cameras = FoVOrthographicCameras(device=device, R=R, T=T, znear=0.01)
|
|
|
|
raster_settings = PointsRasterizationSettings(
|
|
# Set image_size so it is not a multiple of 16 (min bin_size)
|
|
# in order to confirm that there are no errors in coarse rasterization.
|
|
image_size=500,
|
|
radius=0.003,
|
|
points_per_pixel=10,
|
|
)
|
|
|
|
renderer = PointsRenderer(
|
|
rasterizer=PointsRasterizer(
|
|
cameras=cameras, raster_settings=raster_settings
|
|
),
|
|
compositor=AlphaCompositor(),
|
|
)
|
|
|
|
images = renderer(point_cloud)
|
|
|
|
# Load reference image
|
|
filename = "bridge_pointcloud.png"
|
|
image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
|
|
|
|
for bin_size in [0, None]:
|
|
# Check both naive and coarse to fine produce the same output.
|
|
renderer.rasterizer.raster_settings.bin_size = bin_size
|
|
images = renderer(point_cloud)
|
|
rgb = images[0, ..., :3].squeeze().cpu()
|
|
if DEBUG:
|
|
filename = "DEBUG_%s" % filename
|
|
Image.fromarray((rgb.detach().numpy() * 255).astype(np.uint8)).save(
|
|
DATA_DIR / filename
|
|
)
|
|
self.assertClose(rgb, image_ref, atol=0.015)
|
|
|
|
# Check grad exists.
|
|
grad_images = torch.randn_like(images)
|
|
images.backward(grad_images)
|
|
self.assertIsNotNone(verts.grad)
|
|
self.assertIsNotNone(rgb_feats.grad)
|
|
|
|
def test_simple_sphere_batched(self):
|
|
device = torch.device("cuda:0")
|
|
sphere_mesh = ico_sphere(1, device)
|
|
verts_padded = sphere_mesh.verts_padded()
|
|
verts_padded[..., 1] += 0.2
|
|
verts_padded[..., 0] += 0.2
|
|
pointclouds = Pointclouds(
|
|
points=verts_padded, features=torch.ones_like(verts_padded)
|
|
)
|
|
batch_size = 20
|
|
pointclouds = pointclouds.extend(batch_size)
|
|
R, T = look_at_view_transform(2.7, 0.0, 0.0)
|
|
cameras = FoVPerspectiveCameras(device=device, R=R, T=T)
|
|
raster_settings = PointsRasterizationSettings(
|
|
image_size=256, radius=5e-2, points_per_pixel=1
|
|
)
|
|
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
|
|
compositor = NormWeightedCompositor()
|
|
renderer = PointsRenderer(rasterizer=rasterizer, compositor=compositor)
|
|
|
|
# Load reference image
|
|
filename = "simple_pointcloud_sphere.png"
|
|
image_ref = load_rgb_image("test_%s" % filename, DATA_DIR)
|
|
|
|
images = renderer(pointclouds)
|
|
for i in range(batch_size):
|
|
rgb = images[i, ..., :3].squeeze().cpu()
|
|
if i == 0 and DEBUG:
|
|
filename = "DEBUG_%s" % filename
|
|
Image.fromarray((rgb.numpy() * 255).astype(np.uint8)).save(
|
|
DATA_DIR / filename
|
|
)
|
|
self.assertClose(rgb, image_ref)
|
|
|
|
def test_compositor_background_color_rgba(self):
|
|
|
|
N, H, W, K, C, P = 1, 15, 15, 20, 4, 225
|
|
ptclds = torch.randn((C, P))
|
|
alphas = torch.rand((N, K, H, W))
|
|
pix_idxs = torch.randint(-1, 20, (N, K, H, W)) # 20 < P, large amount of -1
|
|
background_color = [0.5, 0, 1]
|
|
|
|
compositor_funcs = [
|
|
(NormWeightedCompositor, norm_weighted_sum),
|
|
(AlphaCompositor, alpha_composite),
|
|
]
|
|
|
|
for (compositor_class, composite_func) in compositor_funcs:
|
|
|
|
compositor = compositor_class(background_color)
|
|
|
|
# run the forward method to generate masked images
|
|
masked_images = compositor.forward(pix_idxs, alphas, ptclds)
|
|
|
|
# generate unmasked images for testing purposes
|
|
images = composite_func(pix_idxs, alphas, ptclds)
|
|
|
|
is_foreground = pix_idxs[:, 0] >= 0
|
|
|
|
# make sure foreground values are unchanged
|
|
self.assertClose(
|
|
torch.masked_select(masked_images, is_foreground[:, None]),
|
|
torch.masked_select(images, is_foreground[:, None]),
|
|
)
|
|
|
|
is_background = ~is_foreground[..., None].expand(-1, -1, -1, C)
|
|
|
|
# permute masked_images to correctly get rgb values
|
|
masked_images = masked_images.permute(0, 2, 3, 1)
|
|
for i in range(3):
|
|
channel_color = background_color[i]
|
|
|
|
# check if background colors are properly changed
|
|
self.assertTrue(
|
|
masked_images[is_background]
|
|
.view(-1, C)[..., i]
|
|
.eq(channel_color)
|
|
.all()
|
|
)
|
|
|
|
# check background color alpha values
|
|
self.assertTrue(
|
|
masked_images[is_background].view(-1, C)[..., 3].eq(1).all()
|
|
)
|
|
|
|
def test_compositor_background_color_rgb(self):
|
|
|
|
N, H, W, K, C, P = 1, 15, 15, 20, 3, 225
|
|
ptclds = torch.randn((C, P))
|
|
alphas = torch.rand((N, K, H, W))
|
|
pix_idxs = torch.randint(-1, 20, (N, K, H, W)) # 20 < P, large amount of -1
|
|
background_color = [0.5, 0, 1]
|
|
|
|
compositor_funcs = [
|
|
(NormWeightedCompositor, norm_weighted_sum),
|
|
(AlphaCompositor, alpha_composite),
|
|
]
|
|
|
|
for (compositor_class, composite_func) in compositor_funcs:
|
|
|
|
compositor = compositor_class(background_color)
|
|
|
|
# run the forward method to generate masked images
|
|
masked_images = compositor.forward(pix_idxs, alphas, ptclds)
|
|
|
|
# generate unmasked images for testing purposes
|
|
images = composite_func(pix_idxs, alphas, ptclds)
|
|
|
|
is_foreground = pix_idxs[:, 0] >= 0
|
|
|
|
# make sure foreground values are unchanged
|
|
self.assertClose(
|
|
torch.masked_select(masked_images, is_foreground[:, None]),
|
|
torch.masked_select(images, is_foreground[:, None]),
|
|
)
|
|
|
|
is_background = ~is_foreground[..., None].expand(-1, -1, -1, C)
|
|
|
|
# permute masked_images to correctly get rgb values
|
|
masked_images = masked_images.permute(0, 2, 3, 1)
|
|
for i in range(3):
|
|
channel_color = background_color[i]
|
|
|
|
# check if background colors are properly changed
|
|
self.assertTrue(
|
|
masked_images[is_background]
|
|
.view(-1, C)[..., i]
|
|
.eq(channel_color)
|
|
.all()
|
|
)
|