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055ab3a2e3
Summary:
Converts the directory specified to use the Ruff formatter in pyfmt
ruff_dog
If this diff causes merge conflicts when rebasing, please run
`hg status -n -0 --change . -I '**/*.{py,pyi}' | xargs -0 arc pyfmt`
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That should help reduce or eliminate any merge conflicts.
allow-large-files
Reviewed By: bottler
Differential Revision: D66472063
fbshipit-source-id: 35841cb397e4f8e066e2159550d2f56b403b1bef
95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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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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import imageio
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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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import torch
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from pytorch3d.renderer.points.pulsar import Renderer
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from torch import nn
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from torch.autograd import Variable
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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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