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#!/usr/bin/env python3
# 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.
"""Test right hand/left hand system compatibility."""
import sys
import unittest
from os import path
import numpy as np
import torch
from torch import nn
sys.path.insert(0, path.join(path.dirname(__file__), ".."))
devices = [torch.device("cuda"), torch.device("cpu")]
n_points = 10
width = 1_000
height = 1_000
class SceneModel(nn.Module):
"""A simple model to demonstrate use in Modules."""
def __init__(self):
super(SceneModel, self).__init__()
from pytorch3d.renderer.points.pulsar import Renderer
self.gamma = 1.0
# Points.
torch.manual_seed(1)
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=False))
self.register_parameter(
"vert_col",
nn.Parameter(
torch.zeros(1, n_points, 3, dtype=torch.float32), requires_grad=True
),
)
self.register_parameter(
"vert_rad",
nn.Parameter(
torch.ones(1, n_points, dtype=torch.float32) * 0.001,
requires_grad=False,
),
)
self.register_parameter(
"vert_opy",
nn.Parameter(
torch.ones(1, n_points, dtype=torch.float32), requires_grad=False
),
)
self.register_buffer(
"cam_params",
torch.tensor(
[
[
np.sin(angle) * 35.0,
0.0,
30.0 - np.cos(angle) * 35.0,
0.0,
-angle,
0.0,
5.0,
2.0,
]
for angle in [-1.5, -0.8, -0.4, -0.1, 0.1, 0.4, 0.8, 1.5]
],
dtype=torch.float32,
),
)
self.renderer = Renderer(width, height, n_points)
def forward(self, cam=None):
if cam is None:
cam = self.cam_params
n_views = 8
else:
n_views = 1
return self.renderer.forward(
self.vert_pos.expand(n_views, -1, -1),
self.vert_col.expand(n_views, -1, -1),
self.vert_rad.expand(n_views, -1),
cam,
self.gamma,
45.0,
return_forward_info=True,
)
class TestSmallSpheres(unittest.TestCase):
"""Test small sphere rendering and gradients."""
def test_basic(self):
for device in devices:
# Set up model.
model = SceneModel().to(device)
angle = 0.0
for _ in range(50):
cam_control = torch.tensor(
[
[
np.sin(angle) * 35.0,
0.0,
30.0 - np.cos(angle) * 35.0,
0.0,
-angle,
0.0,
5.0,
2.0,
]
],
dtype=torch.float32,
).to(device)
result, forw_info = model(cam=cam_control)
sphere_ids = model.renderer.sphere_ids_from_result_info_nograd(
forw_info
)
# Assert all spheres are rendered.
for idx in range(n_points):
self.assertTrue(
(sphere_ids == idx).sum() > 0, "Sphere ID %d missing!" % (idx)
)
# 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)
# Back-propagate some dummy gradients.
loss = ((result - torch.ones_like(result)).abs()).sum()
loss.backward()
# Now check whether the gradient arrives at every sphere.
self.assertTrue(torch.all(model.vert_col.grad[:, :, 0].abs() > 0.0))
angle += 0.15
if __name__ == "__main__":
unittest.main()
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