mirror of
https://github.com/facebookresearch/pytorch3d.git
synced 2025-08-02 03:42:50 +08:00
b19fe1de2f
Summary: This diff integrates the pulsar renderer source code into PyTorch3D as an alternative backend for the PyTorch3D point renderer. This diff is the first of a series of three diffs to complete that migration and focuses on the packaging and integration of the source code. For more information about the pulsar backend, see the release notes and the paper (https://arxiv.org/abs/2004.07484). For information on how to use the backend, see the point cloud rendering notebook and the examples in the folder `docs/examples`. Tasks addressed in the following diffs: * Add the PyTorch3D interface, * Add notebook examples and documentation (or adapt the existing ones to feature both interfaces). Reviewed By: nikhilaravi Differential Revision: D23947736 fbshipit-source-id: a5e77b53e6750334db22aefa89b4c079cda1b443
140 lines
4.3 KiB
Python
140 lines
4.3 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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"""Test right hand/left hand system compatibility."""
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import sys
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import unittest
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from os import path
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import numpy as np
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import torch
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from torch import nn
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sys.path.insert(0, path.join(path.dirname(__file__), ".."))
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devices = [torch.device("cuda"), torch.device("cpu")]
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n_points = 10
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width = 1_000
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height = 1_000
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class SceneModel(nn.Module):
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"""A simple model to demonstrate use in Modules."""
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def __init__(self):
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super(SceneModel, self).__init__()
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from pytorch3d.renderer.points.pulsar import Renderer
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self.gamma = 1.0
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# Points.
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torch.manual_seed(1)
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vert_pos = torch.rand((1, n_points, 3), dtype=torch.float32) * 10.0
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vert_pos[:, :, 2] += 25.0
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vert_pos[:, :, :2] -= 5.0
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self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False))
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self.register_parameter(
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"vert_col",
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nn.Parameter(
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torch.zeros(1, n_points, 3, dtype=torch.float32), requires_grad=True
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),
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)
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self.register_parameter(
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"vert_rad",
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nn.Parameter(
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torch.ones(1, n_points, dtype=torch.float32) * 0.001,
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requires_grad=False,
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),
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)
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self.register_parameter(
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"vert_opy",
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nn.Parameter(
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torch.ones(1, n_points, dtype=torch.float32), requires_grad=False
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),
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)
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self.register_buffer(
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"cam_params",
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torch.tensor(
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[
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[
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np.sin(angle) * 35.0,
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0.0,
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30.0 - np.cos(angle) * 35.0,
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0.0,
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-angle,
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0.0,
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5.0,
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2.0,
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]
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for angle in [-1.5, -0.8, -0.4, -0.1, 0.1, 0.4, 0.8, 1.5]
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],
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dtype=torch.float32,
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),
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)
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self.renderer = Renderer(width, height, n_points)
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def forward(self, cam=None):
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if cam is None:
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cam = self.cam_params
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n_views = 8
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else:
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n_views = 1
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return self.renderer.forward(
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self.vert_pos.expand(n_views, -1, -1),
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self.vert_col.expand(n_views, -1, -1),
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self.vert_rad.expand(n_views, -1),
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cam,
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self.gamma,
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45.0,
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return_forward_info=True,
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)
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class TestSmallSpheres(unittest.TestCase):
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"""Test small sphere rendering and gradients."""
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def test_basic(self):
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for device in devices:
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# Set up model.
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model = SceneModel().to(device)
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angle = 0.0
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for _ in range(50):
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cam_control = torch.tensor(
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[
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[
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np.sin(angle) * 35.0,
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0.0,
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30.0 - np.cos(angle) * 35.0,
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0.0,
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-angle,
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0.0,
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5.0,
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2.0,
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]
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],
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dtype=torch.float32,
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).to(device)
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result, forw_info = model(cam=cam_control)
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sphere_ids = model.renderer.sphere_ids_from_result_info_nograd(
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forw_info
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)
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# Assert all spheres are rendered.
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for idx in range(n_points):
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self.assertTrue(
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(sphere_ids == idx).sum() > 0, "Sphere ID %d missing!" % (idx)
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)
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# Visualize.
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# result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
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# cv2.imshow("res", result_im[0, :, :, ::-1])
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# cv2.waitKey(0)
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# Back-propagate some dummy gradients.
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loss = ((result - torch.ones_like(result)).abs()).sum()
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loss.backward()
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# Now check whether the gradient arrives at every sphere.
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self.assertTrue(torch.all(model.vert_col.grad[:, :, 0].abs() > 0.0))
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angle += 0.15
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if __name__ == "__main__":
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unittest.main()
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