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https://github.com/facebookresearch/pytorch3d.git
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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
89 lines
2.8 KiB
Python
89 lines
2.8 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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"""Create multiview data."""
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import sys
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from os import path
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# Making sure you can run this, even if pulsar hasn't been installed yet.
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sys.path.insert(0, path.join(path.dirname(__file__), "..", ".."))
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def create_multiview():
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"""Test multiview optimization."""
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from pytorch3d.renderer.points.pulsar import Renderer
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import torch
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from torch import nn
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import imageio
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from torch.autograd import Variable
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# import cv2
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# import skvideo.io
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import numpy as np
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# Constructor.
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n_points = 10
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width = 1000
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height = 1000
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class Model(nn.Module):
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"""A dummy model to test the integration into a stacked model."""
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def __init__(self):
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super(Model, self).__init__()
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self.gamma = 0.1
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self.renderer = Renderer(width, height, n_points)
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def forward(self, vp, vc, vr, cam_params):
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# self.gamma *= 0.995
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# print("gamma: ", self.gamma)
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return self.renderer.forward(vp, vc, vr, cam_params, self.gamma, 45.0)
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# Generate sample data.
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torch.manual_seed(1)
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vert_pos = torch.rand(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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# print(vert_pos[0])
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vert_col = torch.rand(n_points, 3, dtype=torch.float32)
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vert_rad = torch.rand(n_points, dtype=torch.float32)
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# Distortion.
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# vert_pos[:, 1] += 0.5
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vert_col *= 0.5
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# vert_rad *= 0.7
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for device in [torch.device("cuda")]:
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model = Model().to(device)
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vert_pos = vert_pos.to(device)
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vert_col = vert_col.to(device)
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vert_rad = vert_rad.to(device)
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for angle_idx, angle in enumerate([-1.5, -0.8, -0.4, -0.1, 0.1, 0.4, 0.8, 1.5]):
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vert_pos_v = Variable(vert_pos, requires_grad=False)
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vert_col_v = Variable(vert_col, requires_grad=False)
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vert_rad_v = Variable(vert_rad, requires_grad=False)
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cam_params = torch.tensor(
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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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dtype=torch.float32,
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).to(device)
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cam_params_v = Variable(cam_params, requires_grad=False)
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result = model.forward(vert_pos_v, vert_col_v, vert_rad_v, cam_params_v)
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result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
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imageio.imsave(
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"reference/examples_TestRenderer_test_multiview_%d.png" % (angle_idx),
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result_im,
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)
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if __name__ == "__main__":
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create_multiview()
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